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Journal articles on the topic 'Data and information privacy'

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Published: 10 December 2022

Last updated: 28 January 2023

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1

Fletcher, Sam, and Md Zahidul Islam. "Measuring Information Quality for Privacy Preserving Data Mining." International Journal of Computer Theory and Engineering 7, no. 1 (February 2014): 21–28. http://dx.doi.org/10.7763/ijcte.2015.v7.924.

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2

Gertner, Yael, Yuval Ishai, Eyal Kushilevitz, and Tal Malkin. "Protecting Data Privacy in Private Information Retrieval Schemes." Journal of Computer and System Sciences 60, no. 3 (June 2000): 592–629. http://dx.doi.org/10.1006/jcss.1999.1689.

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3

Mihai Yiannaki, Simona, Lucia Gibilaro, and Gianluca Mattarocci. "Big data firms and information privacy." Global Business and Economics Review 25, no. 3/4 (2021): 355. http://dx.doi.org/10.1504/gber.2021.10042256.

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4

Yiannaki, Simona Mihai, Lucia Gibilaro, and Gianluca Mattarocci. "Big data firms and information privacy." Global Business and Economics Review 25, no. 3/4 (2021): 355. http://dx.doi.org/10.1504/gber.2021.118713.

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5

Anderson, R. "Undermining data privacy in health information." BMJ 322, no. 7284 (February 24, 2001): 442–43. http://dx.doi.org/10.1136/bmj.322.7284.442.

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6

Suleiman, James, and Terry Huston. "Data Privacy and Security." International Journal of Information Security and Privacy 3, no. 2 (April 2009): 42–53. http://dx.doi.org/10.4018/jisp.2009040103.

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7

White, Garry L., Francis A. Méndez Mediavilla, and Jaymeen R. Shah. "Information Privacy." International Journal of Information Security and Privacy 5, no. 1 (January 2011): 50–66. http://dx.doi.org/10.4018/jisp.2011010104.

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In the Web dependent world, companies must respect and protect individuals’ information privacy. Companies develop and implement corporate information privacy policies to comply with the domestic and international information privacy laws and regulations. This paper investigates: (a) the approach used by multinational and domestic companies to develop and implement corporate information privacy policies; and (b) the perception of corporate managers/professionals toward information privacy legislation and secondary use of personally identifiable information (PII) that organizations collect. A survey was conducted to collect data from corporate CEOs, managers, and technical professionals of national and multinational companies. Findings indicate the following: 1) Views regarding the practicality and effectiveness of information privacy legislations are similar for respondents from the national and multinational companies. 2) Respondents are undecided about whether the privacy laws of the United States and foreign countries are equally restrictive. 3) Multinational companies do not favor developing and implementing uniform information privacy policies or different information privacy policies across countries of operations. 4) Respondents strongly agreed that unauthorized secondary use of personal information is unacceptable.

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8

Agrawal, Rakesh, and Ramakrishnan Srikant. "Privacy-preserving data mining." ACM SIGMOD Record 29, no. 2 (June 2000): 439–50. http://dx.doi.org/10.1145/335191.335438.

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9

Wang, Ting, and Ling Liu. "Output privacy in data mining." ACM Transactions on Database Systems 36, no. 1 (March 2011): 1–34. http://dx.doi.org/10.1145/1929934.1929935.

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10

Hu, Haibo, Jianliang Xu, Sai Tung On, Jing Du, and Joseph Kee-Yin Ng. "Privacy-aware location data publishing." ACM Transactions on Database Systems 35, no. 3 (July 2010): 1–42. http://dx.doi.org/10.1145/1806907.1806910.

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11

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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12

Mondal, Sutapa, Mangesh S. Gharote, and Sachin P. Lodha. "Privacy of Personal Information." Queue 20, no. 3 (June 30, 2022): 41–87. http://dx.doi.org/10.1145/3546934.

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Each online interaction with an external service creates data about the user that is digitally recorded and stored. These external services may be credit card transactions, medical consultations, census data collection, voter registration, etc. Although the data is ostensibly collected to provide citizens with better services, the privacy of the individual is inevitably put at risk. With the growing reach of the Internet and the volume of data being generated, data protection and, specifically, preserving the privacy of individuals, have become particularly important. In this article we discuss the data privacy concepts using two fictitious characters, Swara and Betaal, and their interactions with a fictitious entity, namely Asha Hospital.

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13

Vishnoi, Meenakshi, and Seeja K. R. "Privacy Preserving Data Mining using Attribute Encryption and Data Perturbation." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 6, no. 3 (May 25, 2013): 370–78. http://dx.doi.org/10.24297/ijct.v6i3.4461.

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Data mining is a very active research area that deals with the extraction of Â knowledge from very large databases. Data mining has made knowledge extraction and decision making easy. The extracted knowledge could reveal the personal information , if the data contains various private and sensitive attributes about an individual. This poses a threat to the personal information as there is a possibility of misusing the information behind the scenes without the knowledge of the individual. So, privacy becomes a great concern for the data owners and the organizations Â as none of the organizations would like to share their data. To solve this problem Privacy Preserving Data Mining technique have emerged and also solved problems of various domains as it provides the benefit of data mining without compromising the privacy of an individual. This paper proposes a privacy preserving data mining technique the uses randomized perturbation and cryptographic technique. The performance evaluation of the proposed technique shows the same result with the modified data and the original data.

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14

Lei Xu, Chunxiao Jiang, Jian Wang, Jian Yuan, and Yong Ren. "Information Security in Big Data: Privacy and Data Mining." IEEE Access 2 (2014): 1149–76. http://dx.doi.org/10.1109/access.2014.2362522.

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15

Win, Khin Than, and Willy Susilo. "Information security and privacy of health data." International Journal of Healthcare Technology and Management 7, no. 6 (2006): 492. http://dx.doi.org/10.1504/ijhtm.2006.010413.

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16

Navarro-Arribas, Guillermo, and Vicenç Torra. "Information fusion in data privacy: A survey." Information Fusion 13, no. 4 (October 2012): 235–44. http://dx.doi.org/10.1016/j.inffus.2012.01.001.

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17

Yang, Kwangmo. "Big Technology and Data Privacy." Healthcare Informatics Research 26, no. 3 (July 31, 2020): 163–65. http://dx.doi.org/10.4258/hir.2020.26.3.163.

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18

Shin, Soo-Yong. "Privacy Protection and Data Utilization." Healthcare Informatics Research 27, no. 1 (January 31, 2021): 1–2. http://dx.doi.org/10.4258/hir.2021.27.1.1.

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19

Wang, Pingshui, Jianwen Zhu, and Qinjuan Ma. "Private Data Protection in Social Networks Based on Blockchain." International Journal of Advanced Networking and Applications 14, no. 04 (2023): 5549–55. http://dx.doi.org/10.35444/ijana.2023.14407.

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With the rapid development of big data and social networks, user data in social networks are facing huge risks of privacy leakage. It is urgent to establish a complete and effective method for protecting private data in social networks. Based on the problem of information leakage in social networks, classifies user privacy data, and constructs different privacy data protection schemes through blockchain time stamp recording data storage, hash function anonymous operation of data, asymmetric encryption and digital signature of sending information. The blockchain-based privacy data protection method in social networks can effectively solve the privacy leakage problem in social networks, and provide a reference for the research in the field of information security and social network security. This paper designs a new blockchain-based privacy data protection scheme for different privacy disclosure categories, which provides a new solution to the current privacy disclosure problem in social networks. However, the existing methods will consume a lot of computational power in the process of information interaction. The subsequent research will optimize the computational power of blockchain and try to build a better blockchain social network privacy data protection system.

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20

Jia, Dongning, Bo Yin, and Xianqing Huang. "Association Analysis of Private Information in Distributed Social Networks Based on Big Data." Wireless Communications and Mobile Computing 2021 (June 4, 2021): 1–12. http://dx.doi.org/10.1155/2021/1181129.

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As people’s awareness of the issue of privacy leakage continues to increase, and the demand for privacy protection continues to increase, there is an urgent need for some effective methods or means to achieve the purpose of protecting privacy. So far, there have been many achievements in the research of location-based privacy services, and it can effectively protect the location privacy of users. However, there are few research results that require privacy protection, and the privacy protection system needs to be improved. Aiming at the shortcomings of traditional differential privacy protection, this paper designs a differential privacy protection mechanism based on interactive social networks. Under this mechanism, we have proved that it meets the protection conditions of differential privacy and prevents the leakage of private information with the greatest possibility. In this paper, we establish a network evolution game model, in which users only play games with connected users. Then, based on the game model, a dynamic equation is derived to express the trend of the proportion of users adopting privacy protection settings in the network over time, and the impact of the benefit-cost ratio on the evolutionarily stable state is analyzed. A real data set is used to verify the feasibility of the model. Experimental results show that the model can effectively describe the dynamic evolution of social network users’ privacy protection behaviors. This model can help social platforms design effective security services and incentive mechanisms, encourage users to adopt privacy protection settings, and promote the deployment of privacy protection mechanisms in the network.

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21

Riyana, Surapon, Nobutaka Ito, Tatsanee Chaiya, Uthaiwan Sriwichai, Natthawud Dussadee, Tanate Chaichana, Rittichai Assawarachan, Thongchai Maneechukate, Samerkhwan Tantikul, and Noppamas Riyana. "Privacy Threats and Privacy Preservation Techniques for Farmer Data Collections Based on Data Shuffling." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 3 (June 25, 2022): 289–301. http://dx.doi.org/10.37936/ecti-cit.2022163.246469.

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Aside from smart technologies, farm data collection is also important for smart farms including farm environment data collection and farmer survey data collection. With farm data collection, we observe that it is generally proposed to utilize in smart farm systems. However, it can also be released for use in the outside scope of the data collecting organization for an appropriate business reason such as improving the smart farm system, product quality, and customer service. Moreover, we can observe that the farmer survey data collection often includes sensitive data, the private data of farmers. Thus, it could lead to privacy violation issues when it is released. To address these issues in the farmer survey data collection, an anatomization model can protect the users' private data that is available in farmer survey data collection to be proposed. However, it still has disorganized issues and privacy violation issues in the sensitive table that must be addressed. To rid these vulnerabilities of anatomization models, a new privacy preservation model based on data shuffing is proposed in this work. Moreover, the proposed model is evaluated by conducting extensive experiments. The experimental results indicate that the proposed model is more efficient than the anatomization model for the farmer survey data collection. That is, the adversary can have the confidence for re-identifying every sensitive data that is available in farmer survey data collection that is after satisfied by the privacy preservation constraint of the proposed model to be at most 1/l. Furthermore, after the farmer survey data collection satisfies the privacy preservation constraint of the proposed model, it does not have disorganized issues and privacy violation issues from considering the sensitive values.

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22

Grosskreutz, Henrik, Benedikt Lemmen, and Stefan Rüping. "Privacy-Preserving Data-Mining." Informatik-Spektrum 33, no. 4 (May 28, 2010): 380–83. http://dx.doi.org/10.1007/s00287-010-0447-1.

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23

Duan, Huabin, Jie Yang, and Huanjun Yang. "A Blockchain-Based Privacy Protection Application for Logistics Big Data." Journal of Cases on Information Technology 24, no. 5 (February 21, 2022): 1–12. http://dx.doi.org/10.4018/jcit.295249.

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Logistics business is generally managed by logistics orders in plain text, and there is a risk of disclosure of customer privacy information in every business link. In order to solve the problem of privacy protection in logistics big data system, a new kind of logistics user privacy data protection scheme is proposed. First of all, an access rights management mechanism is designed by combining block chain and anonymous authentication to realize the control and management of users' access rights to private data. Then, the privacy and confidentiality protection between different services is realized by dividing and storing the data of different services. Finally, the participants of the intra-chain private data are specified by embedding fields in the logistics information. The blockchain node receiving the transaction is used as the transit node to synchronize the intra-chain privacy data, so as to improve the intra-chain privacy protection within the business. Experimental results show that the proposed method can satisfy the privacy requirements and ensure considerable performance.

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24

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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25

Falgoust, Michael. "Data Science and Designing for Privacy." Techné: Research in Philosophy and Technology 20, no. 1 (2016): 51–68. http://dx.doi.org/10.5840/techne201632446.

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Unprecedented advances in the ability to store, analyze, and retrieve data is the hallmark of the information age. Along with enhanced capability to identify meaningful patterns in large data sets, contemporary data science renders many classical models of privacy protection ineffective. Addressing these issues through privacy-sensitive design is insufficient because advanced data science is mutually exclusive with preserving privacy. The special privacy problem posed by data analysis has so far escaped even leading accounts of informational privacy. Here, I argue that accounts of privacy must include norms about information processing in addition to norms about information flow. Ultimately, users need the resources to control how and when personal information is processed and the knowledge to make information decisions about that control. While privacy is an insufficient design constraint, value-sensitive design around control and transparency can support privacy in the information age.

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26

Halder, Suhasish, V. S. Prakash Attili, and Vivek Gupta. "Information Privacy Assimilation." International Journal of Digital Strategy, Governance, and Business Transformation 12, no. 1 (January 1, 2022): 1–17. http://dx.doi.org/10.4018/ijdsgbt.313954.

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This paper proposes a framework to understand organizations' perspectives while safeguarding customers' information privacy. Following a detailed literature review, a broad conceptual model was developed to build a theory based on a multi-site, multi-case study approach. The current manuscript treats information privacy as distinct from information security. From an organizational standpoint, this research reveals that legal policy, technology, and industry standards drive privacy assimilation. At a detailed level, adherence to compliance, competitive best practices, and data management controls significantly impact an organization's opportunistic perspective, resulting in higher-order assimilation (infusion) of organizational privacy practices. Resistance to compliance, investment cost, and reactive approach results in lower-order assimilation (adaptation) of organizational privacy practices. This study delivers practical implications related to how businesses perceive privacy practices while maintaining the right balance of privacy risks and opportunities.

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27

Yang, Qing, Cheng Wang, Teng Hu, Xue Chen, and Changjun Jiang. "Implicit privacy preservation: a framework based on data generation." Security and Safety 1 (2022): 2022008. http://dx.doi.org/10.1051/sands/2022008.

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This paper addresses a special and imperceptible class of privacy, called implicit privacy. In contrast to traditional (explicit) privacy, implicit privacy has two essential properties: (1) It is not initially defined as a privacy attribute; (2) it is strongly associated with privacy attributes. In other words, attackers could utilize it to infer privacy attributes with a certain probability, indirectly resulting in the disclosure of private information. To deal with the implicit privacy disclosure problem, we give a measurable definition of implicit privacy, and propose an ex-ante implicit privacy-preserving framework based on data generation, called IMPOSTER. The framework consists of an implicit privacy detection module and an implicit privacy protection module. The former uses normalized mutual information to detect implicit privacy attributes that are strongly related to traditional privacy attributes. Based on the idea of data generation, the latter equips the Generative Adversarial Network (GAN) framework with an additional discriminator, which is used to eliminate the association between traditional privacy attributes and implicit ones. We elaborate a theoretical analysis for the convergence of the framework. Experiments demonstrate that with the learned generator, IMPOSTER can alleviate the disclosure of implicit privacy while maintaining good data utility.

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28

Othman, Nashwan Adnan, and Mustafa Zuhaer Nayef Al-Dabagh. "Privacy Preserving Data Mining Using Random Decision Tree Over Partition Data: Survey." ITM Web of Conferences 42 (2022): 01010. http://dx.doi.org/10.1051/itmconf/20224201010.

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The development of data mining with data protection and data utility can manage distributed data efficiently. This paper revisits the concepts and techniques of privacy-preserving Random Decision Tree (RDT). In existing systems, cryptography-based techniques are effective at managing distributed information. Privacy-preserving RDT handles distributed information efficiently. Privacy-preserving RDT gives better precision data mining while preserving information and reducing the calculation time. This paper deals with this headway in privacy-preserving data mining technology utilizing emphasized approach of RDT. RDT gives preferable productivity and information privacy than cryptographic technique. Various data mining tasks utilize RDT, like classification, relapse, ranking, and different classifications. Privacy-preserving RDT utilizes both randomization and the cryptographic method, giving information privacy for some decision tree-based learning tasks; this is an effective technique for data mining with privacy-preserving distributed information. Thus, in horizontal partitioning of the dataset, parties gather information for various entities but have data for all attributes. On the other hand, various associations may gather different data about a similar set of people. Thus, in vertically partitioned data, all parties gather data for the same collection of items. In all of these cases, both horizontal and vertical partitioning of datasets is somewhat inaccurate.

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29

Chen, Z. F., J. J. Shuai, F. J. Tian, W. Y. Li, S. H. Zang, and X. Z. Zhang. "An Improved Privacy Protection Algorithm for Multimodal Data Fusion." Scientific Programming 2022 (August 23, 2022): 1–7. http://dx.doi.org/10.1155/2022/4189148.

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With the rapid development of Internet technology, the use and sharing of data have brought great opportunities and challenges to mankind. On the one hand, the development of data sharing and analysis technology has promoted the improvement of economic and social benefits. On the other hand, protecting private information has become an urgent issue in the Internet era. In addition, the amount and type of information data are also increasing. At present, most algorithms can only encrypt a single type of small-scale data, which cannot meet the current data environment. Therefore, it is very necessary to study the privacy protection algorithm of multimodal data fusion. To improve the security of privacy protection algorithm, combined with the idea of multimode, this paper combines the improved traditional spatial steganography algorithm LSB matching method and the improved traditional transform domain steganography algorithm DCT with AES encryption algorithm after modifying the S-box and then combines it with image stitching technology, so as to realize a safe and reliable privacy protection algorithm of multimode information fusion. The algorithm completes the hidden communication of private information, which not only ensures that the receiver can accurately recover private information in the process of information transmission but also greatly improves the security of private information transmission.

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30

Thuraisingham, Bhavani. "Privacy-Preserving Data Mining." Journal of Database Management 16, no. 1 (January 2005): 75–87. http://dx.doi.org/10.4018/jdm.2005010106.

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31

Yao-Huai, Lü. "Privacy and Data Privacy Issues in Contemporary China." Ethics and Information Technology 7, no. 1 (March 2005): 7–15. http://dx.doi.org/10.1007/s10676-005-0456-y.

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32

Xu, Xiaolong, Xuan Zhao, Feng Ruan, Jie Zhang, Wei Tian, Wanchun Dou, and Alex X. Liu. "Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds." Security and Communication Networks 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/2376484.

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Nowadays, a large number of groups choose to deploy their applications to cloud platforms, especially for the big data era. Currently, the hybrid cloud is one of the most popular computing paradigms for holding the privacy-aware applications driven by the requirements of privacy protection and cost saving. However, it is still a challenge to realize data placement considering both the energy consumption in private cloud and the cost for renting the public cloud services. In view of this challenge, a cost and energy aware data placement method, named CEDP, for privacy-aware applications over big data in hybrid cloud is proposed. Technically, formalized analysis of cost, access time, and energy consumption is conducted in the hybrid cloud environment. Then a corresponding data placement method is designed to accomplish the cost saving for renting the public cloud services and energy savings for task execution within the private cloud platforms. Experimental evaluations validate the efficiency and effectiveness of our proposed method.

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33

Hendra, Hendra, Ravel Ravel, Novel Firdhaus, Michael Ari Kurniawan, and Gilbert Platina. "E-HEALTH PERSONAL DATA PROTECTION IN INDONESIA." JURNAL HUKUM KESEHATAN INDONESIA 1, no. 02 (April 13, 2022): 121–31. http://dx.doi.org/10.53337/jhki.v1i02.15.

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Since Covid-19 day by day users additional attentive to their privacy and information protection. though this drawback is transverse to each digital service, it's particularly relevant once important and private info is managed, as in eHealth and well-being services. throughout the last years, many alternative innovative services during this space are projected. However, information management challenges area unit still in would like of an answer. In general, information area unit directly sent to services however no trustworthy instruments to recover this information or take away them from services area unit obtainable. In these paper author needs to debate privacy aspects of non-public information within the eHealth program and the way Indonesia regulate Privacy and protection of patient’s personal information in Indonesia. the strategy used is normative juridical approach with descriptive analytical specifications. the method of knowledge assortment is finished through literature.

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34

Gruzd, Anatoliy, Jenna Jacobson, and Elizabeth Dubois. "Cybervetting and the Public Life of Social Media Data." Social Media + Society 6, no. 2 (April 2020): 205630512091561. http://dx.doi.org/10.1177/2056305120915618.

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The article examines whether and how the ever-evolving practice of using social media to screen job applicants may undermine people’s trust in the organizations that are engaging in this practice. Using a survey of 429 participants, we assess whether their comfort level with cybervetting can be explained by the factors outlined by Petronio’s communication privacy management theory: culture, gender, motivation, and risk-benefit ratio. We find that respondents from India are significantly more comfortable with social media screening than those living in the United States. We did not find any gender-based differences in individuals’ comfort with social media screening, which suggests that there may be some consistent set of norms, expectations, or “privacy rules” that apply in the context of employment seeking—irrespective of gender. As a theoretical contribution, we apply the communication privacy management theory to analyze information that is publicly available, which offers a unique extension of the theory that focuses on private information. Importantly, the research suggests that privacy boundaries are not only important when it comes to private information, but also with information that is publicly available on social media. The research identifies that just because social media data are public, does not mean people do not have context-specific and data-specific expectations of privacy.

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35

Blume, Peter. "Danish Data Protection with Respect to Law Libraries." International Journal of Legal Information 31, no. 3 (2003): 452–61. http://dx.doi.org/10.1017/s0731126500003735.

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Data protection and information privacy are essential parts of lex informatica. The purpose of legal rules is to sustain a modern development and adjustment of the fundamental right to privacy, taking the realities of the information society into consideration. The aim is to protect the individual against misuse of personal information that may violate the private sphere and simultaneously to protect against surveillance with the purpose of governing behavior. Privacy protection is furthermore important, since personal information, which always has had economic value to a much larger degree, has become a commodity today. There are many reasons sustaining data protection, and legal regulation is very broad covering all parts of society. Merely a fragment of this issue is being considered in the following.

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36

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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37

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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38

liu, Qiang. "Privacy Protection Technology Based on Machine Learning and Intelligent Data Recognition." Security and Communication Networks 2022 (May 5, 2022): 1–9. http://dx.doi.org/10.1155/2022/1598826.

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The machine learning algorithm is gradually being applied to various fields and has become the core technology to achieve artificial intelligence. The success of machine learning cannot be achieved without the support of large amounts of data and computing power, which are usually collected through crowdsourcing and learned online. The data collected for machine learning training often contains some personal and sensitive information, including personal mobile phone numbers, ID numbers, and medical information. How to protect these private data at low cost and efficiently is an important problem. Aiming at this kind of problem, this article starts with the privacy problem in machine learning and the way of being attacked and summarizes the privacy protection methods and characteristics in the machine learning algorithm. Then, for the classification accuracy of the different algorithm that uses noise to protect privacy, a deep difference privacy protection method combined with a convolutional neural network is proposed. This method perfectly integrates the features of difference and Gaussian distribution and can obtain the privacy budget of each layer of the neural network. Finally, the stochastic gradient descent algorithm's gradient value is employed to set the Gaussian noise scale and preserve the data's sensitive information. The experimental results demonstrated that by adjusting the parameters of the depth differential privacy model based on differences in private information in the data, a balance between the availability and privacy protection of the training data set could be reached.

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Huerta, Esperanza, and Scott Jensen. "An Accounting Information Systems Perspective on Data Analytics and Big Data." Journal of Information Systems 31, no. 3 (May 1, 2017): 101–14. http://dx.doi.org/10.2308/isys-51799.

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ABSTRACT Forty-six academics and practitioners participated in the second Journal of Information Systems Conference to discuss data analytics and Big Data from an accounting information systems perspective. The panels discussed the evolving role of technology in accounting, privacy within the domain of Big Data, and people and Big Data. Throughout all three panels, several topics emerged that impact all areas of accounting—developing enhanced analytical and data handling skills; evaluating privacy, security requirements, and risks; thinking creatively; and assessing the threat of automation to the accounting profession. Other topics were specific to a segment of the profession, such as the growing demand for privacy compliance audits and the curriculum adjustments necessary to develop data analytic skills. This commentary synthesizes and expands the discussions of the conference panels and suggests potential areas for future research.

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Olusola Olajide, Ajayi,. "Application of Data Masking in Achieving Information Privacy." IOSR Journal of Engineering 4, no. 2 (February 2014): 13–21. http://dx.doi.org/10.9790/3021-04211321.

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Mai, Jens-Erik. "Big data privacy: The datafication of personal information." Information Society 32, no. 3 (April 13, 2016): 192–99. http://dx.doi.org/10.1080/01972243.2016.1153010.

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Choi, Jay Pil, Doh-Shin Jeon, and Byung-Cheol Kim. "Privacy and personal data collection with information externalities." Journal of Public Economics 173 (May 2019): 113–24. http://dx.doi.org/10.1016/j.jpubeco.2019.02.001.

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Navarro-Arribas, Guillermo, and Vicenç Torra. "Information fusion in the context of data privacy." Information Fusion 13, no. 4 (October 2012): 234. http://dx.doi.org/10.1016/j.inffus.2012.01.002.

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Schroeder, Alan T. "Book review: Data privacy in the information age." Journal of the American Society for Information Science and Technology 53, no. 3 (2002): 251–53. http://dx.doi.org/10.1002/asi.10037.

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He, Jianping, Lin Cai, and Xinping Guan. "Preserving Data-Privacy With Added Noises: Optimal Estimation and Privacy Analysis." IEEE Transactions on Information Theory 64, no. 8 (August 2018): 5677–90. http://dx.doi.org/10.1109/tit.2018.2842221.

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Rajalakshmi, M., T. Purusothaman, and S. Pratheeba. "Collusion-Free Privacy Preserving Data Mining." International Journal of Intelligent Information Technologies 6, no. 4 (October 2010): 30–45. http://dx.doi.org/10.4018/jiit.2010100103.

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Distributed association rule mining is an integral part of data mining that extracts useful information hidden in distributed data sources. As local frequent itemsets are globalized from data sources, sensitive information about individual data sources needs high protection. Different privacy preserving data mining approaches for distributed environment have been proposed but in the existing approaches, collusion among the participating sites reveal sensitive information about the other sites. In this paper, the authors propose a collusion-free algorithm for mining global frequent itemsets in a distributed environment with minimal communication among sites. This algorithm uses the techniques of splitting and sanitizing the itemsets and communicates to random sites in two different phases, thus making it difficult for the colluders to retrieve sensitive information. Results show that the consequence of collusion is reduced to a greater extent without affecting mining performance and confirms optimal communication among sites.

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Domingo-Ferrer, Josep, and Vicenç Torra. "Privacy in Data Mining." Data Mining and Knowledge Discovery 11, no. 2 (August 17, 2005): 117–19. http://dx.doi.org/10.1007/s10618-005-0009-3.

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Wang, Yi-Ren, and Yun-Cheng Tsai. "The Protection of Data Sharing for Privacy in Financial Vision." Applied Sciences 12, no. 15 (July 23, 2022): 7408. http://dx.doi.org/10.3390/app12157408.

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The primary motivation is to address difficulties in data interpretation or a reduction in model accuracy. Although differential privacy can provide data privacy guarantees, it also creates problems. Thus, we need to consider the noise setting for differential privacy is currently inconclusive. This paper’s main contribution is finding a balance between privacy and accuracy. The training data of deep learning models may contain private or sensitive corporate information. These may be dangerous to attacks, leading to privacy data leakage for data sharing. Many strategies are for privacy protection, and differential privacy is the most widely applied one. Google proposed a federated learning technology to solve the problem of data silos in 2016. The technology can share information without exchanging original data and has made significant progress in the medical field. However, there is still the risk of data leakage in federated learning; thus, many models are now used with differential privacy mechanisms to minimize the risk. The data in the financial field are similar to medical data, which contains a substantial amount of personal data. The leakage may cause uncontrollable consequences, making data exchange and sharing difficult. Let us suppose that differential privacy applies to the financial field. Financial institutions can provide customers with higher value and personalized services and automate credit scoring and risk management. Unfortunately, the economic area rarely applies differential privacy and attains no consensus on parameter settings. This study compares data security with non-private and differential privacy financial visual models. The paper finds a balance between privacy protection with model accuracy. The results show that when the privacy loss parameter ϵ is between 12.62 and 5.41, the privacy models can protect training data, and the accuracy does not decrease too much.

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Stoll, Margareth. "A Data Privacy Governance Model." International Journal of IT/Business Alignment and Governance 10, no. 1 (January 2019): 74–93. http://dx.doi.org/10.4018/ijitbag.2019010105.

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The importance of data privacy, information availability and integrity are increasingly recognized. The new EU general data protection regulation 679/2016 obligates stringent legal requirements with high sanctions for noncompliance. Most organizations worldwide are affected directly or indirectly. It requires overall a risk and evidence-based data privacy management as part of corporate governance. More than 1.6 million organizations worldwide are implementing a standard-based management system, such as ISO 9001 or others. To implement the new data protection regulation in an effective, efficient and sustainable way, the author provides design-oriented guidelines on how to integrate the legal requirements into standard based management systems. The holistic data privacy governance model integrates different information security governance frameworks with standard based management systems in order to comply the regulation. In that way data privacy is part of all strategic, tactical and operational business processes, promotes corporate governance, legal compliance and living data protection.

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Hodge, James G. "Health Information Privacy and Public Health." Journal of Law, Medicine & Ethics 31, no. 4 (2003): 663–71. http://dx.doi.org/10.1111/j.1748-720x.2003.tb00133.x.

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Protecting the privacy of individually-identifiable health data and promoting the public’s health often seem at odds. Privacy advocates consistently seek to limit the acquisition, use, and disclosure of identifiable health information in governmental and private sector settings. Their concerns relate to misuses or wrongful disclosures of sensitive health data that can lead to discrimination and stigmatization against individuals. Public health practitioners, on the other hand, seek regular, ongoing access to and use of identifiable health information to accomplish important public health objectives. The collection and use of identifiable health data by federal, tribal, state, and local health authorities support nearly all public health functions and goals.Identifiable health data are the lifeblood of public health practice. When aggregated, these data help authorities monitor the incidence, patterns, and trends of injury and disease in populations. Health data are acquired by public health authorities through testing, screening, and treatment programs.

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