Journal articles on the topic 'Private data publishing'
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Al-Hussaeni, Khalil, Benjamin C. M. Fung, Farkhund Iqbal, Junqiang Liu, and Patrick C. K. Hung. "Differentially private multidimensional data publishing." Knowledge and Information Systems 56, no. 3 (November 24, 2017): 717–52. http://dx.doi.org/10.1007/s10115-017-1132-3.
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Sramka, Michal. "Data mining as a tool in privacy-preserving data publishing." Tatra Mountains Mathematical Publications 45, no. 1 (December 1, 2010): 151–59. http://dx.doi.org/10.2478/v10127-010-0011-z.
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ABSTRACTMany databases contain data about individuals that are valuable for research, marketing, and decision making. Sharing or publishing data about individuals is however prone to privacy attacks, breaches, and disclosures. The concern here is about individuals’ privacy-keeping the sensitive information about individuals private to them. Data mining in this setting has been shown to be a powerful tool to breach privacy and make disclosures. In contrast, data mining can be also used in practice to aid data owners in their decision on how to share and publish their databases. We present and discuss the role and uses of data mining in these scenarios and also briefly discuss other approaches to private data analysis.
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Xu, Yong, Shan Ying Zhou, and Yu Tao Sun. "Study on Privacy Preserving Technology in Data Publishing Scenario." Applied Mechanics and Materials 170-173 (May 2012): 3658–61. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3658.
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In recent years, many data sets are accessed for the purposes of research, cooperation and e-business, and so on. Publishing data about individuals without revealing their private information has become an active issue, and k-Anonymous-based models are effective techniques that prevent linking attack. We analyzed the privacy leakage problem in data publishing environment. Then we concluded the privacy preserving technologies, and clarified the k-anonymity models. Finally we conclude the directions of this area.
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Zhu, Tianqing, Gang Li, Wanlei Zhou, and Philip S. Yu. "Differentially Private Data Publishing and Analysis: A Survey." IEEE Transactions on Knowledge and Data Engineering 29, no. 8 (August 1, 2017): 1619–38. http://dx.doi.org/10.1109/tkde.2017.2697856.
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Parra-Arnau, Javier, Josep Domingo-Ferrer, and Jordi Soria-Comas. "Differentially private data publishing via cross-moment microaggregation." Information Fusion 53 (January 2020): 269–88. http://dx.doi.org/10.1016/j.inffus.2019.06.011.
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Liu, Xinyao, Baojiang Cui, Junsong Fu, Zishuai Cheng, and Xuyan Song. "Secure Data Publishing of Private Trajectory in Edge Computing of IoT." Security and Communication Networks 2022 (June 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/2045586.
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Secure data publishing of private trajectory is a typical application scene in the Internet of Things (IoT). Protecting users’ privacy while publishing data has always been a long-term challenge. In recent years, the mainstream method is to combine the Markov model and differential privacy (DP) mechanism to build a private trajectory generation model and publishes the generated synthetic trajectory data instead of the original data. However, Markov cannot effectively model the long-term trajectory data spatio-temporal correlation, and the DP noise results in the low availability of the synthetic data. To protect users’ privacy and improve the availability of synthetic trajectory data, we propose a trajectory generation model with differential privacy and deep learning (DTG). In DTG, we design a private hierarchical adaptive grid method. It divides the geospatial region into several subregions according to the density of positions to realize the discretization of coordinates of the trajectory data. Second, GRU is used to capture the temporal features of the trajectory sequence for good availability, and we generate synthetic trajectory data by predicting the next position. Third, we adopt the optimizer perturbation method in gradient descent to protect the privacy of model parameters. Finally, we experimentally compare DTG with the state-of-the-art approaches in trajectory generation on actual trajectory data T-Drive, Portotaxi, and Swedishtaxi. The result demonstrates that DTG has a better performance in generating synthetic trajectories under four error metrics.
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Geetha, Dr V., Dr C. K. Gomathy*, Mr Maddu Pavan Manikanta Kiran, and Mr Gandikota Rajesh. "A Secure Based Preserving Social Media Data Management System." International Journal of Engineering and Advanced Technology 10, no. 4 (April 30, 2021): 210–14. http://dx.doi.org/10.35940/ijeat.d2455.0410421.
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Personalized suggestions are important to help users find relevant information. It often depends on huge collection of user data, especially users’ online activity (e.g., liking/commenting/sharing) on social media, thereto user interests. Publishing such user activity makes inference attacks easy on the users, as private data (e.g., contact details) are often easily gathered from the users’ activity data. during this module, we proposed PrivacyRank, an adjustable and always protecting privacy on social media data publishing framework , which protects users against frequent attacks while giving personal ranking based recommendations. Its main idea is to continuously blur user activity data like user-specified private data is minimized under a given data budget, which matches round the ranking loss suffer from the knowledge blurring process so on preserve the usage of the info for enabling suggestions. a true world evaluation on both synthetic and real-world datasets displays that our model can provide effective and continuous protection against to the info given by the user, while still conserving the usage of the blurred data for private ranking based suggestion. Compared to other approaches, Privacy Rank achieves both better privacy protection and a far better usage altogether the rank based suggestions use cases we tested.
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Yan, Liang, Hao Wang, Zhaokun Wang, Tingting Wu, Wandi Fu, and Xu Zhang. "Differentially Private Timestamps Publishing in Trajectory." Electronics 12, no. 2 (January 10, 2023): 361. http://dx.doi.org/10.3390/electronics12020361.
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In recent years, location-based social media has become popular, and a large number of spatiotemporal trajectory data have been generated. Although these data have significant mining value, they also pose a great threat to the privacy of users. At present, many studies have realized the privacy-preserving mechanism of location data in social media in terms of data utility and privacy preservation, but rarely have any of them considered the correlation between timestamps and geographical location. To solve this problem, in this paper, we first propose a k-anonymity-based mechanism to hide the user’s specific time segment during a single day, and then propose an optimized truncated Laplacian mechanism to add noise to each data grid (the frequency of time data) of the anonymized time distribution. The time data after secondary processing are fuzzy and uncertain, which not only protects the privacy of the user’s geographical location from the time dimension but also retains a certain value of data mining. Experiments on real datasets show that the TDP privacy-preserving model has good utility.
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Qamar, T., N. Z. Bawany, and N. A. Khan. "EDAMS: Efficient Data Anonymization Model Selector for Privacy-Preserving Data Publishing." Engineering, Technology & Applied Science Research 10, no. 2 (April 4, 2020): 5423–27. http://dx.doi.org/10.48084/etasr.3374.
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The evolution of internet to the Internet of Things (IoT) gives an exponential rise to the data collection process. This drastic increase in the collection of a person’s private information represents a serious threat to his/her privacy. Privacy-Preserving Data Publishing (PPDP) is an area that provides a way of sharing data in their anonymized version, i.e. keeping the identity of a person undisclosed. Various anonymization models are available in the area of PPDP that guard privacy against numerous attacks. However, selecting the optimum model which balances utility and privacy is a challenging process. This study proposes the Efficient Data Anonymization Model Selector (EDAMS) for PPDP which generates an optimized anonymized dataset in terms of privacy and utility. EDAMS inputs the dataset with required parameters and produces its anonymized version by incorporating PPDP techniques while balancing utility and privacy. EDAMS is currently incorporating three PPDP techniques, namely k-anonymity, l-diversity, and t-closeness. It is tested against different variations of three datasets. The results are validated by testing each variation explicitly with the stated techniques. The results show the effectiveness of EDAMS by selecting the optimum model with minimal effort.
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Du, Jiawen, and Yong Pi. "Research on Privacy Protection Technology of Mobile Social Network Based on Data Mining under Big Data." Security and Communication Networks 2022 (January 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/3826126.
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With the advent of the era of big data, people’s lives have undergone earth-shaking changes, not only getting rid of the cumbersome traditional data collection but also collecting and sorting information directly from people’s footprints on social networks. This paper explores and analyzes the privacy issues in current social networks and puts forward the protection strategies of users’ privacy data based on data mining algorithms so as to truly ensure that users’ privacy in social networks will not be illegally infringed in the era of big data. The data mining algorithm proposed in this paper can protect the user’s identity from being identified and the user’s private information from being leaked. Using differential privacy protection methods in social networks can effectively protect users’ privacy information in data publishing and data mining. Therefore, it is of great significance to study data publishing, data mining methods based on differential privacy protection, and their application in social networks.
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Yang, Gaoming, Xinxin Ye, Xianjin Fang, Rongshi Wu, and Li Wang. "Associated Attribute-Aware Differentially Private Data Publishing via Microaggregation." IEEE Access 8 (2020): 79158–68. http://dx.doi.org/10.1109/access.2020.2990296.
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CAO, Yang, and Masatoshi YOSHIKAWA. "Differentially Private Real-Time Data Publishing over Infinite Trajectory Streams." IEICE Transactions on Information and Systems E99.D, no. 1 (2016): 163–75. http://dx.doi.org/10.1587/transinf.2015edp7096.
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CHEONG, CHI HONG, DAN WU, and MAN HON WONG. "QUANTIFYING PRIVATE INFORMATION WITH HUMAN FACTORS IN DATA PUBLISHING FOR BINARY SENSITIVE ATTRIBUTES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 05 (October 2010): 645–76. http://dx.doi.org/10.1142/s0218488510006738.
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Human factors play a key role in many problems, especially those involve human. In this paper, we address the problem of quantifying private information with the consideration of human factors in data publishing for binary sensitive attributes. We first propose five axioms to capture the properties of private information in data publishing. In particular, the fifth axiom considers human factors in the view of social psychology. We believe that a function that quantifies private information has to satisfy all these five axioms. Therefore, we propose an expected gain model, which allows users to tune a weighting factor to reflect the importance of human factors in their applications. The proposed model has been analyzed and compared with some existing models, such as information gain. Moreover, experiments have been performed on real applications to study the practicality of the proposed model.
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Hong, Yiyang, Xingwen Zhao, Hui Zhu, and Hui Li. "A Blockchain-Integrated Divided-Block Sparse Matrix Transformation Differential Privacy Data Publishing Model." Security and Communication Networks 2021 (December 7, 2021): 1–15. http://dx.doi.org/10.1155/2021/2418539.
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With the rapid development of information technology, people benefit more and more from big data. At the same time, it becomes a great concern that how to obtain optimal outputs from big data publishing and sharing management while protecting privacy. Many researchers seek to realize differential privacy protection in massive high-dimensional datasets using the method of principal component analysis. However, these algorithms are inefficient in processing and do not take into account the different privacy protection needs of each attribute in high-dimensional datasets. To address the above problem, we design a Divided-block Sparse Matrix Transformation Differential Privacy Data Publishing Algorithm (DSMT-DP). In this algorithm, different levels of privacy budget parameters are assigned to different attributes according to the required privacy protection level of each attribute, taking into account the privacy protection needs of different levels of attributes. Meanwhile, the use of the divided-block scheme and the sparse matrix transformation scheme can improve the computational efficiency of the principal component analysis method for handling large amounts of high-dimensional sensitive data, and we demonstrate that the proposed algorithm satisfies differential privacy. Our experimental results show that the mean square error of the proposed algorithm is smaller than the traditional differential privacy algorithm with the same privacy parameters, and the computational efficiency can be improved. Further, we combine this algorithm with blockchain and propose an Efficient Privacy Data Publishing and Sharing Model based on the blockchain. Publishing and sharing private data on this model not only resist strong background knowledge attacks from adversaries outside the system but also prevent stealing and tampering of data by not-completely-honest participants inside the system.
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Jiang, Yili, Kuan Zhang, Yi Qian, and Liang Zhou. "Reinforcement-Learning-Based Query Optimization in Differentially Private IoT Data Publishing." IEEE Internet of Things Journal 8, no. 14 (July 15, 2021): 11163–76. http://dx.doi.org/10.1109/jiot.2021.3052978.
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Zhao, Jing, Shubo Liu, Xingxing Xiong, and Zhaohui Cai. "Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window." Security and Communication Networks 2021 (April 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/6665984.
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Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.
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Ho, Stella, Youyang Qu, Bruce Gu, Longxiang Gao, Jianxin Li, and Yong Xiang. "DP-GAN: Differentially private consecutive data publishing using generative adversarial nets." Journal of Network and Computer Applications 185 (July 2021): 103066. http://dx.doi.org/10.1016/j.jnca.2021.103066.
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Soria-Comas, Jordi, and Josep Domingo-Ferrer. "Differentially private data publishing via optimal univariate microaggregation and record perturbation." Knowledge-Based Systems 153 (August 2018): 78–90. http://dx.doi.org/10.1016/j.knosys.2018.04.027.
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Bild, Raffael, Klaus A. Kuhn, and Fabian Prasser. "SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees." Proceedings on Privacy Enhancing Technologies 2018, no. 1 (January 1, 2018): 67–87. http://dx.doi.org/10.1515/popets-2018-0004.
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Abstract Methods for privacy-preserving data publishing and analysis trade off privacy risks for individuals against the quality of output data. In this article, we present a data publishing algorithm that satisfies the differential privacy model. The transformations performed are truthful, which means that the algorithm does not perturb input data or generate synthetic output data. Instead, records are randomly drawn from the input dataset and the uniqueness of their features is reduced. This also offers an intuitive notion of privacy protection. Moreover, the approach is generic, as it can be parameterized with different objective functions to optimize its output towards different applications. We show this by integrating six well-known data quality models. We present an extensive analytical and experimental evaluation and a comparison with prior work. The results show that our algorithm is the first practical implementation of the described approach and that it can be used with reasonable privacy parameters resulting in high degrees of protection. Moreover, when parameterizing the generic method with an objective function quantifying the suitability of data for building statistical classifiers, we measured prediction accuracies that compare very well with results obtained using state-of-the-art differentially private classification algorithms.
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Jafer, Yasser, Stan Matwin, and Marina Sokolova. "Using Feature Selection to Improve the Utility of Differentially Private Data Publishing." Procedia Computer Science 37 (2014): 511–16. http://dx.doi.org/10.1016/j.procs.2014.08.076.
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Shankar, Adam Gowri. "Differential Privacy Preserving in Big data Analytics for Body Area Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 514–18. http://dx.doi.org/10.22214/ijraset.2021.39336.
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Abstract: Body Area Networks (BANs), collects enormous data by wearable sensors which contain sensitive information such as physical condition, location information, and so on, which needs protection. Preservation of privacy in big data has emerged as an absolute prerequisite for exchanging private data in terms of data analysis, validation, and publishing. Previous methods and traditional methods like k-anonymity and other anonymization techniques have overlooked privacy protection issues resulting to privacy infringement. In this work, a differential privacy protection scheme for ‘big data in body area network’ is developed. Compared with previous methods, the proposed privacy protection scheme is best in terms of availability and reliability. Exploratory results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy. Keywords: BAN’s, Privacy, Differential Privacy, Noisy response
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Huang, Wei, Tong Yi, Haibin Zhu, Wenqian Shang, and Weiguo Lin. "Improved privacy preserving method for periodical SRS publishing." PLOS ONE 16, no. 4 (April 22, 2021): e0250457. http://dx.doi.org/10.1371/journal.pone.0250457.
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Spontaneous reporting systems (SRSs) are used to collect adverse drug events (ADEs) for their evaluation and analysis. Periodical SRS data publication gives rise to a problem where sensitive, private data can be discovered through various attacks. The existing SRS data publishing methods are vulnerable to Medicine Discontinuation Attack(MD-attack) and Substantial symptoms-attack(SS-attack). To remedy this problem, an improved periodical SRS data publishing—PPMS(k, θ, ɑ)-bounding is proposed. This new method can recognize MD-attack by ensuring that each equivalence group contains at least k new medicine discontinuation records. The SS-attack can be thwarted using a heuristic algorithm. Theoretical analysis indicates that PPMS(k, θ, ɑ)-bounding can thwart the above-mentioned attacks. The experimental results also demonstrate that PPMS(k, θ, ɑ)-bounding can provide much better protection for privacy than the existing method and the new method dose not increase the information loss. PPMS(k, θ, ɑ)-bounding can improve the privacy, guaranteeing the information usability of the released tables.
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Lv, Haoze, Zhaobin Liu, Zhonglian Hu, Lihai Nie, Weijiang Liu, and Xinfeng Ye. "Research on improved privacy publishing algorithm based on set cover." Computer Science and Information Systems 16, no. 3 (2019): 705–31. http://dx.doi.org/10.2298/csis180915023l.
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With the invention of big data era, data releasing is becoming a hot topic in database community. Meanwhile, data privacy also raises the attention of users. As far as the privacy protection models that have been proposed, the differential privacy model is widely utilized because of its many advantages over other models. However, for the private releasing of multi-dimensional data sets, the existing algorithms are publishing data usually with low availability. The reason is that the noise in the released data is rapidly grown as the increasing of the dimensions. In view of this issue, we propose algorithms based on regular and irregular marginal tables of frequent item sets to protect privacy and promote availability. The main idea is to reduce the dimension of the data set, and to achieve differential privacy protection with Laplace noise. First, we propose a marginal table cover algorithm based on frequent items by considering the effectiveness of query cover combination, and then obtain a regular marginal table cover set with smaller size but higher data availability. Then, a differential privacy model with irregular marginal table is proposed in the application scenario with low data availability and high cover rate. Next, we obtain the approximate optimal marginal table cover algorithm by our analysis to get the query cover set which satisfies the multi-level query policy constraint. Thus, the balance between privacy protection and data availability is achieved. Finally, extensive experiments have been done on synthetic and real databases, demonstrating that the proposed method preforms better than state-of-the-art methods in most cases.
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Wang, Hao, and Kaiju Li. "Resistance of IID Noise in Differentially Private Schemes for Trajectory Publishing." Computer Journal 63, no. 4 (November 17, 2019): 549–66. http://dx.doi.org/10.1093/comjnl/bxz097.
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Abstract Although analyzing and mining user’s trajectory data can provide outstanding benefit, data owners may not be willing to upload their trajectory data because of privacy concerns. Recently, differential privacy technology has achieved a good trade-off between data utility and privacy preserving by publishing noisy outputs, and relevant schemes have been proposed for trajectory release. However, we experimentally find that a relatively accurate estimate of the true data value can still be obtained from the noisy outputs by means of a posterior estimation. But there are no practical mechanisms against current schemes to verify their effectiveness and resistance. To fill this gap, we propose a solution to evaluate the resistance performance of differential privacy on trajectory data release, including a notion of correlation-distinguishability filtering (CDF) and a privacy quantification measurement. Specifically, taking advantage of the principle of filtering that independent noise can be filtered out from correlated sequence, CDF is proposed to sanitize the noise added into the trajectory. To conduct this notion in practice, we attempt to apply a Kalman/particle filter to filter out the corresponding Gaussian/Laplace noise added by differential privacy schemes. Furthermore, to quantify the distortion of privacy strength before and after filtering, an entropy-based privacy quantification metric is proposed, which is used to measure the lost uncertainty of the true locations for an adversary. Experimental results show that the resistance performance of current approaches has a degradation to varying degrees under the filtering attack model in our solution. Moreover, the privacy quantification metric can be regarded as a unified criterion to measure the privacy strength introduced by the noise that does not conform to the form required by differential privacy.
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Al-Zobbi, Mohammed Essa, Seyed Shahrestani, and Chun Ruan. "Achieving Optimal K-Anonymity Parameters for Big Data." International Journal of Information, Communication Technology and Applications 4, no. 1 (May 15, 2018): 23–33. http://dx.doi.org/10.17972/ijicta20184136.
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Datasets containing private and sensitive information are useful for data analytics. Data owners cautiously release such sensitive data using privacy-preserving publishing techniques. Personal re-identification possibility is much larger than ever before. For instance, social media has dramatically increased the exposure to privacy violation. One well-known technique of k-anonymity proposes a protection approach against privacy exposure. K-anonymity tends to find k equivalent number of data records. The chosen attributes are known as Quasi-identifiers. This approach may reduce the personal re-identification. However, this may lessen the usefulness of information gained. The value of k should be carefully determined, to compromise both security and information gained. Unfortunately, there is no any standard procedure to define the value of k. The problem of the optimal k-anonymization is NP-hard. In this paper, we propose a greedy-based heuristic approach that provides an optimal value for k. The approach evaluates the empirical risk concerning our Sensitivity-Based Anonymization method. Our approach is derived from the fine-grained access and business role anonymization for big data, which forms our framework.
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Renuka, S. "A Study of Privacy Preserving Using Anonymization Techniques." Asian Journal of Computer Science and Technology 8, S2 (March 5, 2019): 31–34. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2029.
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Now a day’s there is an extensive use of technology that has led to a massive increase in the amount of data that is generated. The analysis of such information will help the business and organization in various ways and also contributing beneficially to society in many different fields. As this data also contains the considerable amount of user-sensitive and private information, it will lead to the potential threats to the user’s privacy if the data is published without applying any privacy preserving techniques to the data. This paper discusses the various anonymization techniques such as generalization and suppression which are used to preserve privacy during data publishing.
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Hoek, Janneke, Paula O'Kane, and Martin McCracken. "Publishing personal information online." Personnel Review 45, no. 1 (February 1, 2016): 67–83. http://dx.doi.org/10.1108/pr-05-2014-0099.
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Purpose – The purpose of this paper is to examine employers’ use of social networking sites (SNSs) within employee selection. Design/methodology/approach – In-depth interviews were conducted with 15 organisations to gain an understanding of how they accessed, observed and utilised data from SNSs in their selection procedures, as well as gaining an insight into employers’ perceptions of candidate privacy and discrimination. Findings – SNS profiles were either accessed as part of an organisation’s official selection process, through integrating internet screening as part of the formal process and obtaining candidate permission, or through covert (without consent) observation. Facebook was primarily used to identify a candidate’s organisation fit and make assessment of their soft skills, whereas LinkedIn distinguished their professional attributes and their job fit. Problems were associated with the extent to which SNSs were reflective of the person and whether a candidate’s personal life reflected their work persona. Respondents focused more upon the legality, rather than the ethics, of accessing “private” information via SNSs. Research limitations/implications – Further research is needed to consider the content and predictive validity of SNSs as a selection tool before their utility can be ascertained. Practical implications – Organisations should have a clear goal when utilising SNSs, be aware of the value of the information and consider how it complements other selection tools. Selectors should have integrity throughout the selection process, view SNSs as a support tool and use their common sense. Originality/value – The in-depth nature of this research enabled the authors to understand how and why organisations are currently utilising SNSs within selection.
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Jo, Geonhyoung, Kangsoo Jung, and Seog Park. "An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream." Journal of Advanced Transportation 2018 (October 29, 2018): 1–11. http://dx.doi.org/10.1155/2018/8297678.
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Recently, various services based on user's location are emerging since the development of wireless Internet and sensor technology. VANET (vehicular ad hoc network), in which a large number of vehicles communicate using wireless communication, is also being highlighted as one of the services. VANET collects and analyzes the traffic data periodically to provide the traffic information service. The problem is that traffic data contains user’s sensitive location information that can lead to privacy violations. Differential privacy techniques are being used as a de facto standard to prevent such privacy violation caused by data analysis. However, applying differential privacy to traffic data stream which has infinite size over time makes data useless because too much noise is inserted to protect privacy. In order to overcome this limitation, existing researches set a certain range of windows and apply differential privacy to windowed data. However, previous researches have set a fixed window size do not consider a traffic data’s property such as road structure and time-based traffic variation. It may lead to insufficient privacy protection and unnecessary data utility degradation. In this paper, we propose an adaptive window size selection method that consider the correlation between road networks and time-based traffic variation to solve a fixed window size problem. And we suggest an adjustable privacy budget allocation technique for corresponding to the adaptive window size selection. We show that the proposed method improves the data utility, while satisfying the equal level of differential privacy as compared with the existing method through experiments that is designed based on real-world road network.
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Zhu, Wei. "Personal Information Security Environment Monitoring and Law Protection Using Big Data Analysis." Journal of Environmental and Public Health 2022 (October 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/1558161.
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This article explores the causes of the issues with personal data privacy and outlines the limitations of China’s current legal framework. This article makes the argument that, in the information age, self-discipline and legal protection should be combined in order to safeguard personal information safety. It also makes specific recommendations for strengthening legal protection. This research also develops a data processing platform for data safety and privacy protection while studying the technology of data safety environment monitoring and privacy protection. This work develops optimization methodologies, such as dynamic privacy budget allocation, to increase the model’s speed of convergence and the calibre of the generated data. It adjusts to various privacy and timeliness needs under the assumption that the objective of selective matching of private safety will be satisfied. According to the experimental findings, this algorithm’s accuracy can reach 96.27%. This method enhances the model’s speed of convergence and the calibre of the data created, and it addresses the flaw that the present data fusion publishing procedure cannot withstand the attack of background information. The study’s findings can serve as a starting point for future work on data security and the protection of personal information.
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Tropp, Elsa-Maria, Thomas Hoffmann, and Archil Chochia. "Open Data: A Stepchild in e-Estonia’s Data Management Strategy?" TalTech Journal of European Studies 12, no. 1 (May 1, 2022): 123–44. http://dx.doi.org/10.2478/bjes-2022-0006.
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Abstract The availability of open data has increased dramatically, partly in reaction to several types of government agencies publishing their raw data. Access to and use of open data is not only essential for the development of public policy and delivery of various services, but it is also of eminent value for private (and often economic) purposes. To meet these demands, the availability of open data has increased dramatically both domestically and EU-wide. Nevertheless, it is still access to and use of personal data which is usually in the spotlight of public—and also legal—debates. Contributing to fill this gap, this paper analyses the significance of open data and the resulting challenges imposed by the widespread lack of specific open data policies. The paper also provides an overview of the existing systems used in Estonian governance to ensure access to open information, but also highlights the shortcomings, before it finally makes proposals on how to improve open data disclosure practices in Estonia.
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Dymora, Paweł, Mirosław Mazurek, and Bartosz Kowal. "Open data – an introduction to the issue." ITM Web of Conferences 21 (2018): 00017. http://dx.doi.org/10.1051/itmconf/20182100017.
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Rapidly developing of internet technologies and digitalization of government generate more and more data. Databases from various public institutions and private sectors, e.g. in the fields of economics, transport, environment and public safety are publishing in the global Internet network, so that any user can browse them without additional charges. Most of this data is published on the open data portals. Open data - that is, “open”, public data can allow the processing and analysis of information contained in them completely free of charge. This article is an introduction to a fairly new area of issues such as “open data” or “open government”, presents the main mechanisms of accessing to data in public open data portals and also propose a conceptual open data/government model.
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Yao, Xin, Juan Yu, Jianmin Han, Jianfeng Lu, Hao Peng, Yijia Wu, and Xiaoqian Cao. "DP-CSM: Efficient Differentially Private Synthesis for Human Mobility Trajectory with Coresets and Staircase Mechanism." ISPRS International Journal of Geo-Information 11, no. 12 (December 5, 2022): 607. http://dx.doi.org/10.3390/ijgi11120607.
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Generating differentially private synthetic human mobility trajectories from real trajectories is a commonly used approach for privacy-preserving trajectory publishing. However, existing synthetic trajectory generation methods suffer from the drawbacks of poor scalability and suboptimal privacy–utility trade-off, due to continuous spatial space, high dimentionality of trajectory data and the suboptimal noise addition mechanism. To overcome the drawbacks, we propose DP-CSM, a novel differentially private trajectory generation method using coreset clustering and the staircase mechanism, to generate differentially private synthetic trajectories in two main steps. Firstly, it generates generalized locations for each timestamp, and utilizes coreset-based clustering to improve scalability. Secondly, it reconstructs synthetic trajectories with the generalized locations, and uses the staircase mechanism to avoid the over-perturbation of noises and maintain utility of synthetic trajectories. We choose three state-of-the-art clustering-based generation methods as the comparative baselines, and conduct comprehensive experiments on three real-world datasets to evaluate the performance of DP-CSM. Experimental results show that DP-CSM achieves better privacy–utility trade-off than the three baselines, and significantly outperforms the three baselines in terms of efficiency.
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Panchenko, A. M., and Yu V. Timofeeva. "Publication of scientific journals in the Siberian Branch of the Russian Academy of Sciences in 2001–2010." Proceedings of SPSTL SB RAS, no. 1 (April 20, 2022): 23–35. http://dx.doi.org/10.20913/2618-7515-2022-1-23-35.
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The publication of scientific journals performs a number of functions, including that of state importance, which, in turn, embrace the country image formation and determination of its place on the international arena by demonstrating the level of scientific and economic potential development of the State. Therefore, the process of publishing and studying scientific journals is an actual task. This article considers the publication of scientific journals in SB RAS in the period of 2001–2010 thereby clarifying and complementing to the picture of the regional and all-Russian scientific book publishing. The source base is representative as, based on the current archives of the Siberian enterprise “Science” RAS (printing house No. 4) and the Siberian publishing company “Nauka” of RAS, the analysis of normative-legislative documents, prepared by the Presidium of SB RAS and the Scientific-Publishing Council of SB RAS, regulating organizational and financial aspects of the Branch activities for the publication of scientific journals in the period under study, has been made. Besides, all issues of the weekly newspaper SB RAS “Science in Siberia” for the period of 2001– 2010, 24 scientific journals published by the Publishing House of SB RAS (ISO RAS), which founder was SB RAS together with institutes, as well as scientific journals published in scientific institutes and institutions of SB RAS, universities, private publishing houses have been viewed. The role of the Scientific-Publishing Council of SB RAS (INSO SB RAS) as a permanent advisory body of SB RAS Presidium, which formed and implemented into practice the strategic tasks of publishing, is presented. The discrepant role of English-language versions of journals, which, on the one hand, contribute to the entry of Russian science on the international arena and its integration into the global science, present its results to the world community, forming a positive image of the country, on the other hand, demand considerable labor and financial expenses, is disclosed. The obtained data allow revealing and characterizing the basic structural divisions of SB RAS publishing and printing base for issuing scientific journals (Publishing House of SB RAS, publishing houses of scientific centers, institutes and institutions of SB RAS, universities), and composing the list of private publishing houses, attracted by the Branch for these objectives.
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Alrajhi, Ahmed Naser, and Necati Aydin. "Determinants of effective university–business collaboration." Journal of Industry-University Collaboration 1, no. 3 (October 14, 2019): 169–80. http://dx.doi.org/10.1108/jiuc-05-2019-0011.
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Purpose The attention to the university–business collaboration (UBC) for its role in the knowledge-based economy is growing in many countries. In this context, the purpose of this paper is to conduct two surveys to explore the causes of low collaboration between the private sector and academia in the Kingdom of Saudi Arabia. Design/methodology/approach The first survey covers nearly 50 companies to learn their perspectives. Using the findings of the first survey, a second survey was conducted of university researchers to understand the determinants of private and public funding of research and development projects. The survey provided two types of data, namely, categorical and continuous, which were subjected to reliability and normality tests. A linear regression analysis also was utilized to explore the role of different factors on the funded projects by the two sectors. Findings There is a perception among researchers that the private sector is woefully underestimating research capacity of Saudi universities. One interesting finding is that publishing in journals from the International Scientific Indexing (ISI) is a strong predictor for government funding, but not for private funding. From the private sector perspective, publishing in ISI-indexed journals is not sufficient evidence of research capability. Moreover, high teaching load is a major obstacle in acquiring private funding, but not so for public funding. Practical implications The paper provides two main recommendations to improve collaboration. First, universities should incentivize publishing in high-impact journals more than in ISI-indexed journals to increase the faculty’s research capabilities. Second, universities should reduce the teaching load of faculty involved in research projects, particularly those funded by the private sector. Originality/value The outcomes of this survey-based study are very valuable to the ecosystem of academia, business and government in general and for Saudi Arabia in particular, where there is a vital need to implement the right policies regarding UBC in the country.
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Sowmyarani C. N., Veena Gadad, and Dayananda P. "(p+, α, t)-Anonymity Technique Against Privacy Attacks." International Journal of Information Security and Privacy 15, no. 2 (April 2021): 68–86. http://dx.doi.org/10.4018/ijisp.2021040104.
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Privacy preservation is a major concern in current technology where enormous amounts of data are being collected and published for carrying out analysis. These data may contain sensitive information related to individual who owns them. If the data is published in their original form, they may lead to privacy disclosure which threats privacy requirements. Hence, the data should be anonymized before publishing so that it becomes challenging for intruders to obtain sensitive information by means of any privacy attack model. There are popular data anonymization techniques such as k-anonymity, l-diversity, p-sensitive k-anonymity, (l, m, d) anonymity, and t-closeness, which are vulnerable to different privacy attacks discussed in this paper. The proposed technique called (p+, α, t)-anonymity aims to anonymize the data in such a way that even though intruder has sufficient background knowledge on the target individual he will not be able to infer anything and breach private information. The anonymized data also provide sufficient data utility by allowing various data analytics to be performed.
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Vann, Katie. "Surplus and Indicator." Engaging Science, Technology, and Society 3 (February 17, 2017): 92. http://dx.doi.org/10.17351/ests2017.113.
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This essay offers a perspective on journal impact factor (JIF) centrism in academic evaluation from the vantage point of academic publishing in an increasingly data-driven scholarly environment. The political implications and orientations to the JIF are thought through with respect both to commercial publishing industry consolidation and to the reliance of public-sector scholarly communities on (oligopolistic) commercial academic publishing houses. The author proposes that centrism to the JIF as a legitimizing indicator and incentivizing norm leads to two diametrically opposed forms of “surplus”: for academic communities, surplus emerges in the form of layers of scholarly knowledge effects/impact and labor, which, because they remain foreclosed to formal professional recognition, are inadvertently reconstructed as dispensable (waste); for private sector publishing companies––whose contribution to the publishing process consists foremost in providing scalable content management/distribution platforms and in transforming unique manuscript content into standardized digital objects that are amenable to indexing, aggregation, and comparative calculation––surplus emerges in the form of monetary surplus (profit). The essay describes the inner workings of these phenomena.
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Geck, Caroline. "Data Planet." Charleston Advisor 22, no. 1 (July 1, 2020): 14–18. http://dx.doi.org/10.5260/chara.22.1.14.
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This comprehensive and growing subscription database from SAGE Publishing is a repository of over 157 billion data points and 12.6 billion updated datasets from over 500 United States and international source providers and 80 vendors. This repository has aggregated and organized data from disparate, but authoritative, public, private, and commercial sources. The data is then transformed into a homogeneous library and informational product created with features, such as 37 metadata fields, to enhance organization and searchability. Subscribers, especially in the United States and in fields such as academia, business, and government and policy-making, can quickly access archived data using either one of two different powerful interfaces or alternatively, access data using the 287 hyperlinked Libguides or library guides and create Web pages of data called datasheets that serve as focal points for analyzing statistics of interest. Individuals are offered a variety of functions that assist with analyses and research, such as customizations, integrations, visualizations, and citing. For niche business research needs and for additional fees, the core resource can be bundled with any of the seven premium database products from high quality vendors. These databases include China Yearly Statistics, EASI Market Planner, InfoGroup Business USA, International Equities and Metals, Claritas Consumer Profiles, Claritas Financial and Insurance CLOUT™, and Quarterly Workforce Indicators.
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Kim, Jong, Yon Chung, and Jong Kim. "Differentially Private and Skew-Aware Spatial Decompositions for Mobile Crowdsensing." Sensors 18, no. 11 (October 30, 2018): 3696. http://dx.doi.org/10.3390/s18113696.
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Mobile Crowdsensing (MCS) is a paradigm for collecting large-scale sensor data by leveraging mobile devices equipped with small and low-powered sensors. MCS has recently received considerable attention from diverse fields, because it can reduce the cost incurred in the process of collecting a large amount of sensor data. However, in the task assignment process in MCS, to allocate the requested tasks efficiently, the workers need to send their specific location to the requester, which can raise serious location privacy issues. In this paper, we focus on the methods for publishing differentially a private spatial histogram to guarantee the location privacy of the workers. The private spatial histogram is a sanitized spatial index where each node represents the sub-regions and contains the noisy counts of the objects in each sub-region. With the sanitized spatial histograms, it is possible to estimate approximately the number of workers in the arbitrary area, while preserving their location privacy. However, the existing methods have given little concern to the domain size of the input dataset, leading to the low estimation accuracy. This paper proposes a partitioning technique SAGA (Skew-Aware Grid pArtitioning) based on the hotspots, which is more appropriate to adjust the domain size of the dataset. Further, to optimize the overall errors, we lay a uniform grid in each hotspot. Experimental results on four real-world datasets show that our method provides an enhanced query accuracy compared to the existing methods.
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Shakeel, Shafaq, Adeel Anjum, Alia Asheralieva, and Masoom Alam. "k-NDDP: An Efficient Anonymization Model for Social Network Data Release." Electronics 10, no. 19 (October 8, 2021): 2440. http://dx.doi.org/10.3390/electronics10192440.
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With the evolution of Internet technology, social networking sites have gained a lot of popularity. People make new friends, share their interests, experiences in life, etc. With these activities on social sites, people generate a vast amount of data that is analyzed by third parties for various purposes. As such, publishing social data without protecting an individual’s private or confidential information can be dangerous. To provide privacy protection, this paper proposes a new degree anonymization approach k-NDDP, which extends the concept of k-anonymity and differential privacy based on Node DP for vertex degrees. In particular, this paper considers identity disclosures on social data. If the adversary efficiently obtains background knowledge about the victim’s degree and neighbor connections, it can re-identify its victim from the social data even if the user’s identity is removed. The contribution of this paper is twofold. First, a simple and, at the same time, effective method k–NDDP is proposed. The method is the extension of k-NMF, i.e., the state-of-the-art method to protect against mutual friend attack, to defend against identity disclosures by adding noise to the social data. Second, the achieved privacy using the concept of differential privacy is evaluated. An extensive empirical study shows that for different values of k, the divergence produced by k-NDDP for CC, BW and APL is not more than 0.8%, also added dummy links are 60% less, as compared to k-NMF approach, thereby it validates that the proposed k-NDDP approach provides strong privacy while maintaining the usefulness of data.
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Cheah, Chew Sze, Cheng Ling Tan, and Sook Fern Yeo. "Proactivity among Academicians in Malaysian Private Universities." Environment-Behaviour Proceedings Journal 6, SI4 (July 31, 2021): 95–102. http://dx.doi.org/10.21834/ebpj.v6isi4.2907.
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The study examines demographic variables' role in academicians' proactive work behaviour (PWB) in private universities. Independent sample t-test and one-way Analysis of Variance (ANOVA) were performed using self-reported data from 287 academicians. Results show that academicians demonstrated moderate proactivity level. Male demonstrate higher proactiveness compared to females. Married workers score higher on PWB. An academician who holds a managerial position tends to be more proactive compared to others. Furthermore, Doctor of Philosophy (PhD) holder displays a higher level of productivity. The findings offer practical suggestions to the university to address the situation and delegate job assignments based on individual differences. Keywords: proactive work behaviour; academicians; demographic variables; private university eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6iSI4.2907
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Dimodugno, M., S. Hallman, M. Plaisent, and P. Bernard. "The effect of privacy concerns, risk, control, and trust on individuals’ decisions to share personal information: A game theory-based approach." Journal of Physics: Conference Series 2090, no. 1 (November 1, 2021): 012017. http://dx.doi.org/10.1088/1742-6596/2090/1/012017.
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Abstract The rapid developments and innovations in technology have created unlimited opportunities for private and public organizations to collect, store and analyze the large and complex information about users and their online activities. Data mining, data publishing, and sharing sensitive data with third parties help organizations improve the quality of their products and services and raise significant individuals’ privacy concerns. Privacy of personal information remains subject to considerable controversy. The problem is that big data analytics methods allow user’s data to be unlawfully generated, stored, and processed by leaving users with little to no control over their personal information. This quantitative correlational study measures the effect of privacy concerns, risk, control, and trust on individuals’ decisions to share personal information in the context of big data analysis. The key research question aimed to examine the relationship among the variables of perceived privacy concerns, perceived privacy risk, perceived privacy control, and trust. Drawing on Game Theory, the study explores all the game players’ actions, strategies, and payoffs. Correlation analysis was used to test these variables based on the research model with 418 internet users of e-services in the United States. The overall correlation analysis showed that the variables were significantly related. Recommendations for future studies are to explore e-commerce, e-government, and social networking separately, and data should be collected in different regions where many factors can affect the privacy concerns of the individuals.
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Wright, Sue. "Regional or Minority Languages on the WWW." Journal of Language and Politics 5, no. 2 (September 15, 2006): 189–216. http://dx.doi.org/10.1075/jlp.5.2.04wri.
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This paper reports on research sponsored by Unesco to provide reliable data on the extent to which the WWW is becoming a means for minority language groups to publish information and reach the general public. These are the first findings of what is intended to be a world wide enquiry. We report on the Web presence of a group of European languages, all of which have minority status in the states in which they are spoken. They are various dialects of Occitan3 in France, Sardinian, Piemontese and Ladin in Italy and Frisian in the Netherlands. The research confirms that these languages are used extensively on the Internet. However, it also finds that the domains in which they are used are quite restricted and mirror to a large degree the situation in traditional print publishing. Thus the WWW may only be having an influence on volume of publishing and is not necessarily extending the use of the languages to new areas. Thirdly, it records substantial publishing by private individuals and finds that there are possible consequences here for standardisation of minority languages. The research is comparative and ongoing and will explore whether the European situation is typical or exceptional.
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Srivastava, Rajshree, and Kritika Rani. "A Technological Survey on Privacy Preserving Data Publishing." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 721–27. http://dx.doi.org/10.31142/ijtsrd3587.
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Strasser, Carly, and Eesha Khare. "Estimated effects of implementing an open access policy for grantees at a private foundation." PeerJ 5 (September 26, 2017): e3853. http://dx.doi.org/10.7717/peerj.3853.
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BackgroundThe Gordon and Betty Moore Foundation (GBMF) was interested in understanding the potential effects of requiring that grantees publish their peer-reviewed research in open access journals.MethodsWe collected data on more than 2,000 publications in over 500 journals that were generated by GBMF grantees since 2001. We then examined the journal policies to establish how two possible open access policies might have affected grantee publishing habits.ResultsWe found that 99.3% of the articles published by grantees would have complied with a policy that requires open access within 12 months of publication. We also estimated the maximum annual costs to GBMF for covering fees associated with “gold open access” to be between $400,000 and $2,600,000 annually.DiscussionBased in part on this study, GBMF has implemented a new open access policy that requires grantees make peer-reviewed publications fully available within 12 months.
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Krüger, Anne K. "Quantification 2.0? Bibliometric Infrastructures in Academic Evaluation." Politics and Governance 8, no. 2 (April 9, 2020): 58–67. http://dx.doi.org/10.17645/pag.v8i2.2575.
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Due to developments recently termed as ‘audit,’ ‘evaluation,’ or ‘metric society,’ universities have become subject to ratings and rankings and researchers are evaluated according to standardized quantitative indicators such as their publication output and their personal citation scores. Yet, this development is not only based on the rise of new public management and ideas on ‘the return on public or private investment.’ It has also profited from ongoing technological developments. Due to a massive increase in digital publishing corresponding with the growing availability of related data bibliometric infrastructures for evaluating science are continuously becoming more differentiated and elaborate. They allow for new ways of using bibliometric data through various easily applicable tools. Furthermore, they also produce new quantities of data due to new possibilities in following the digital traces of scientific publications. In this article, I discuss this development as quantification 2.0. The rise of digital infrastructures for publishing, indexing, and managing scientific publications has not only made bibliometric data become a valuable source for performance assessment. It has triggered an unprecedented growth in bibliometric data production turning freely accessible data about scientific work into edited databases and producing competition for its users. The production of bibliometric data has thus become decoupled from their application. Bibliometric data have turned into a self-serving end while their providers are constantly seeking for new tools to make use of them.
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Kang, Peng, Wenzhong Yang, and Jiong Zheng. "Blockchain Private File Storage-Sharing Method Based on IPFS." Sensors 22, no. 14 (July 7, 2022): 5100. http://dx.doi.org/10.3390/s22145100.
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Under the current national network environment, anyone can participate in publishing. As an important information resource, knowledge files reflect the workload of publishers. Moreover, high-quality knowledge files can promote the progress of society. However, pirated inferior files have the opposite effect. At present, most organizations use centralized servers to centrally manage the knowledge files released by users. In addition, it is necessary to introduce an untrusted third party to examine and encrypt the contents of files, which leads to an opaque process of file storage transactions, tampering with intellectual copyright, and the inability to have consistent systems of file management among institutions due to the lack of uniform standards for the same intellectual files. The purpose of this paper is to ensure the safe storage of knowledge files on the one hand and to realize efficient sharing of copyrighted files on the other hand. Therefore, this paper combines NDN (Named Data Network) technology with a distributed blockchain and an Interplanetary File System (IPFS) and proposes a blockchain knowledge file storage and sharing method based on an NDN. The method uses the NDN itself for the file content signature and encryption, thereby separating the file security and transmission process. At the same time, the method uses a flexible NDN reverse path forwarding and routing strategy, combining an IPFS private storage network to improve the safety of the encrypted data storage security. Finally, the method takes advantage of all participating nodes consensus and shares files in the synchronized blockchain to ensure traceability. This paper introduces the structure and principles of the method and describes the process of file upload and transfer. Finally, the performance of the method is compared and evaluated, and the advantages and disadvantages of the method and the future research direction are summarized.
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Shaon, Arif, Armin Straube, and Krishna Roy Chowdhury. "Setting up a National Research Data Curation Service for Qatar: Challenges and Opportunities." International Journal of Digital Curation 12, no. 2 (April 2, 2018): 146–56. http://dx.doi.org/10.2218/ijdc.v12i2.515.
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Over the past decade, Qatar has been making considerable progress towards developing a sustainable research culture for the nation. The main driver behind Qatar’s progress in research and innovation is Qatar Foundation for Education, Science, and Community Development (QF), a private, non-profit organization that aims to utilise research as a catalyst for expanding, diversifying and improving the country’s economy, health and environment. While this has resulted in a significant growth in the number of research publications produced by Qatari researchers in recent years, a nationally co-ordinated approach is needed to address some of the emerging but increasingly important aspects of research data curation, such as management and publication of research data as important outputs, and their long-term digital preservation. Qatar National Library (QNL), launched in November 2012 under the umbrella of QF, aims to establish itself as a centre of excellence in Qatar for research data management, curation and publishing to address the research data-related needs of Qatari researchers and academics. This paper describes QNL’s approach towards establishing a national research data curation service for Qatar, highlighting the associated opportunities and key challenges.
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Al-Aufi, Ali Saif, Nabhan Al-Harrasi, Shahid Al-Balushi, and Hamed Al-Azri. "Mapping out Arab international book fairs." Information and Learning Science 118, no. 5/6 (May 8, 2017): 280–97. http://dx.doi.org/10.1108/ils-02-2017-0008.
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Purpose The purpose of this study is to investigate the status, challenges and impacts of the Arab international book fairs, with particular focus on the Muscat Book Fair. This study proposes a framework to assist in the design and organisation of future Arab book fairs. Design/methodology/approach The study used a variety of methods to obtain data, including questionnaires, site visits (both regional and international), observations and interviews. A literature review was also undertaken which helped determine major worldwide issues related to aspects of book fairs and the publishing industry, focusing on Arab book fairs. Findings Arab international book fairs remain relatively traditional, however, there are several examples of innovative improvements in some states. Reading habits and literacy trends were found to be influenced by dominant socio-cultural factors, emphasising religion and children’s literature. This seems to have a reverse effect on the publishing industry. Results also revealed a number of disadvantages related to economic downturns and political instability. Despite continuous expansion, Arab book fairs still suffer from various obstacles which affect the publishing industry’s growth. There are other obstacles that they face which are directly associated with distribution and marketing as well as violations of intellectual property laws. Practical implications This study proposes a framework for future improvement of the Muscat Book Fair. It discusses the engagement of local cultural institutions, non-profit community and academic organisations, as well as private sector organisations. These will leverage value and help keep abreast of international developments in book fairs/publishing. It is hoped that the proposed framework will be beneficial to those running book fairs at a regional level, and to countries with developing economies. Originality/value There have been no previous empirical studies investigating book fairs in the Arab states. This study adds to the currently scarce body of literature related to book fairs and the publishing industry.
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Nunez-del-Prado, Miguel, Yoshitomi Maehara-Aliaga, Julián Salas, Hugo Alatrista-Salas, and David Megías. "A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns." Applied Sciences 12, no. 4 (February 18, 2022): 2131. http://dx.doi.org/10.3390/app12042131.
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Currently, individuals leave a digital trace of their activities when they use their smartphones, social media, mobile apps, credit card payments, Internet surfing profile, etc. These digital activities hide intrinsic usage patterns, which can be extracted using sequential pattern algorithms. Sequential pattern mining is a promising approach for discovering temporal regularities in huge and heterogeneous databases. These sequences represent individuals’ common behavior and could contain sensitive information. Thus, sequential patterns should be sanitized to preserve individuals’ privacy. Hence, many algorithms have been proposed to accomplish this task. However, these techniques add noise to the candidate support before they are validated as, frequently, and thus, they cannot be applied without having access to all the users’ sequences data. In this paper, we propose a differential privacy graph-based technique for publishing frequent sequential patterns. It is applied at the post-processing stage; hence it may be used to protect frequent sequential patterns after they have been extracted, without the need to access all the users’ sequences. To validate our proposal, we performed a detailed assessment of its utility as a pattern mining algorithm and calculated the impact of the sanitization mechanism on a recommender system. We further evaluated its information loss disclosure risk and performed a comparison with the DP-FSM algorithm.
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Rosnan, Herwina, and Norzayana Yusof. "Medical Tourism from the Perspectives of Industry Players: How Critical is Government Support?" Environment-Behaviour Proceedings Journal 6, SI4 (July 31, 2021): 137–42. http://dx.doi.org/10.21834/ebpj.v6isi4.2912.
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The present study aims to derive the role of the government in developing Malaysian medical tourism. Nine semi-structured interviews were conducted, and data were analysed thematically using Atlas.ti version 8. The study derived four main challenges facing private hospitals and healthcare facilitators, which are regulatory burden, scarcity of health professionals, poor collaboration with state government and low cooperation from other agencies. These challenges then drew the government’s role accordingly. Hence, this article strongly calls for regular dialogues between government agencies, private hospitals and healthcare facilitators to ensure that all stakeholders are on the same page about developing the industry. Keywords: Medical Tourism, Private Hospitals, Healthcare Facilitators, Government eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6iSI4.2912