Journal articles: 'Location- Privacy control'

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Yunxiang, Zhang, and Wang Bin. "Stability Control of Position Flow Fuzzy Estimation in Swarm Intelligence Aware Privacy Protection." Wireless Communications and Mobile Computing 2023 (January 30, 2023): 1–8. http://dx.doi.org/10.1155/2023/4792248.

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The group intelligence perception privacy protection model is a method to achieve the balance between user privacy and service requests through the cooperation between users using location services and has a good perception effect. In order to better protect the location privacy of network users and improve the stability control effect of fuzzy estimation of location flow, this paper designs a stability control method of fuzzy estimation of location flow in group intelligent perception privacy protection. This method uses the group intelligence aware privacy protection model to obtain the user network location coordinates in the group intelligence aware privacy protection. Taking the user’s network location coordinates as input, the location flow queue of multiple users in the group intelligence aware privacy protection network is obtained by the Lyapunov multiobjective location flow estimation queue model. After the fuzzy processing of the user location flow queue, the online control mechanism of location flow fuzzy estimation stability under different conditions is established. According to the online control mechanism, a stability control method based on access control and group intelligence aware task allocation is used to realize the stability control of location flow fuzzy estimation in group intelligence aware privacy protection. The experimental results show that the method can obtain 100% of the user location integrity in the group intelligence aware privacy protection, and the target location flow estimation queue is more accurate. It can effectively reduce the number of communication rounds of fuzzy estimation of location flow in the group intelligence aware privacy protection and has better stability control ability.

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Peng, Tao, Qin Liu, and Guojun Wang. "Enhanced Location Privacy Preserving Scheme in Location-Based Services." IEEE Systems Journal 11, no. 1 (March 2017): 219–30. http://dx.doi.org/10.1109/jsyst.2014.2354235.

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Zhu, Liang, Xiaowei Liu, Zhiyong Jing, Liping Yu, Zengyu Cai, and Jianwei Zhang. "Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks." Electronics 12, no. 1 (December 24, 2022): 70. http://dx.doi.org/10.3390/electronics12010070.

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Location privacy-preserving methods for location-based services in mobile communication networks have received great attention. Traditional location privacy-preserving methods mostly focus on the researches of location data analysis in geographical space. However, there is a lack of studies on location privacy preservation by considering the personalized features of users. In this paper, we present a Knowledge-Driven Location Privacy Preserving (KD-LPP) scheme, in order to mine user preferences and provide customized location privacy protection for users. Firstly, the UBPG algorithm is proposed to mine the basic portrait. User familiarity and user curiosity are modelled to generate psychological portrait. Then, the location transfer matrix based on the user portrait is built to transfer the real location to an anonymous location. In order to achieve customized privacy protection, the amount of privacy is modelled to quantize the demand of privacy protection of target user. Finally, experimental evaluation on two real datasets illustrates that our KD-LPP scheme can not only protect user privacy, but also achieve better accuracy of privacy protection.

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Yang, Guangcan, Shoushan Luo, Yang Xin, Hongliang Zhu, Jingkai Wang, Mingzhen Li, and Yunfeng Wang. "A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks." Applied Sciences 10, no. 23 (November 25, 2020): 8402. http://dx.doi.org/10.3390/app10238402.

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With the advent of intelligent handheld devices, location sharing becomes one of the most popular services in mobile online social networks (mOSNs). In location-sharing services, users can enjoy a better social experience by updating their real-time location information. However, the leakage of private information may hinder the further development of location-sharing services. Although many solutions have been proposed to protect users’ privacy, the privacy-utility trade-offs must be considered. Therefore, we propose a new scheme called search efficient privacy-preserving location-sharing (SELS) system. In our scheme, we create a new approach named associated grids to improve the efficiency of location-sharing systems while maintaining users’ privacy. In addition, by setting the user-defined access control policy proposed in our scheme, users’ flexible privacy-preserving requirements can be satisfied. Detailed complexity and security analysis show that the proposed scheme is a practical and efficient privacy-preserving solution. Extensive simulations are performed to validate the effectiveness and performance of our scheme.

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Perusco, Laura, and Katina Michael. "Control, trust, privacy, and security: evaluating location-based services." IEEE Technology and Society Magazine 26, no. 1 (2007): 4–16. http://dx.doi.org/10.1109/mtas.2007.335564.

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Luceri, Luca, Davide Andreoletti, Massimo Tornatore, Torsten Braun, and Silvia Giordano. "Measurement and control of geo-location privacy on Twitter." Online Social Networks and Media 17 (May 2020): 100078. http://dx.doi.org/10.1016/j.osnem.2020.100078.

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Chu, Xiang, Jun Liu, Daqing Gong, and Rui Wang. "Preserving Location Privacy in Spatial Crowdsourcing Under Quality Control." IEEE Access 7 (2019): 155851–59. http://dx.doi.org/10.1109/access.2019.2949409.

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Qi He, Dapeng Wu, and P. Khosla. "The quest for personal control over mobile location privacy." IEEE Communications Magazine 42, no. 5 (May 2004): 130–36. http://dx.doi.org/10.1109/mcom.2004.1299356.

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Góes, Rômulo Meira, Blake C. Rawlings, Nicholas Recker, Gregory Willett, and Stéphane Lafortune. "Demonstration of Indoor Location Privacy Enforcement using Obfuscation." IFAC-PapersOnLine 51, no. 7 (2018): 145–51. http://dx.doi.org/10.1016/j.ifacol.2018.06.293.

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Xu, Chuan, Li Luo, Yingyi Ding, Guofeng Zhao, and Shui Yu. "Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks." IEEE Wireless Communications Letters 9, no. 10 (October 2020): 1633–37. http://dx.doi.org/10.1109/lwc.2020.2999524.

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Zheng, Lijuan, Linhao Zhang, Meng Cui, Ning Cao, Jianrui Ding, Leul Yalemshet, Tsepo Nyakonda, and Shepard Musasike. "The Research of Mobile Location Privacy Protection Access Control Method Based on Game Theory." Wireless Communications and Mobile Computing 2018 (December 12, 2018): 1–9. http://dx.doi.org/10.1155/2018/1847890.

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In recent years, the Internet of things has developed rapidly. And the location-based service (LBS) is becoming more and more extensive. Service providers hold a large number of users’ information. In order to improve the quality of service, service providers increasingly use big data technology to provide more accurate services for users. At the same time, it aggravates the information disclosure of users’ privacy. From the perspective of the service provider, a mobile location privacy access control method based on game theory is proposed to solve the access control problem of mobile location privacy information. Firstly, the weight coefficient is set according to the location privacy influence factors, and then the access control threshold is calculated according to the privacy location leakage situation of the mobile location. Different visitor levels are set according to the threshold. In the process of access control, the prejudgement of the access behaviour is performed, and then the privacy information amount of the information requested for access is calculated according to the weights of the different information. Compare the result with the threshold and get the access control strategy. The strategy set is selected based on strategy matrix of game theory and thresholds are adjusted based on the calculation returns of strategy matrix. The effectiveness and practicability of the method are verified through the security analysis.

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Xie, Junqing, and Shuai Wang. "A unified location sharing service with end user privacy control." Bell Labs Technical Journal 16, no. 2 (August 26, 2011): 5–20. http://dx.doi.org/10.1002/bltj.20499.

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Tomasin, Stefano, Marco Centenaro, Gonzalo Seco-Granados, Stefan Roth, and Aydin Sezgin. "Location-Privacy Leakage and Integrated Solutions for 5G Cellular Networks and Beyond." Sensors 21, no. 15 (July 30, 2021): 5176. http://dx.doi.org/10.3390/s21155176.

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The 5g of cellular networks improves the precision of user localization and provides the means to disclose location information to ott service providers. The nwdaf can further elaborate this information at an aggregated level using artificial intelligence techniques. These powerful features may lead to the improper use of user location information by mno and ott service providers. Moreover, vulnerabilities at various layers may also leak user location information to eavesdroppers. Hence, the privacy of users is likely at risk, as location is part of their sensitive data. In this paper, we first go through the evolution of localization in cellular networks and investigate their effects on location privacy. Then, we propose a location-privacy-preserving integrated solution comprising virtual private mobile networks, an independent authentication and billing authority, and functions to protect wireless signals against location information leakage. Moreover, we advocate the continuous and detailed control of localization services by the user.

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Li, Yuancheng, Pan Zhang, and Yimeng Wang. "The Location Privacy Protection of Electric Vehicles with Differential Privacy in V2G Networks." Energies 11, no. 10 (October 1, 2018): 2625. http://dx.doi.org/10.3390/en11102625.

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Vehicle-to-grid (V2G) is an important component of smart grids and plays a significant role in improving grid stability, reducing energy consumption and generating cost. However, while electric vehicles are being charged, it is possible to expose the location and movement trajectories of the electric vehicles, thereby triggering a series of privacy and security issues. In response to this problem, we propose a new quadtree-based spatial decomposition algorithm to protect the location privacy of electric vehicles. First of all, we use a random sampling algorithm, which is based on differential privacy, to obtain enough spatial data to achieve the balance between large-scale spatial data and the amount of noise. Secondly, in order to overcome the shortcomings of using tree height to control Laplacian noise in the quadtree, we use sparse vector technology to control the noise added to the tree nodes. Finally, according to the vehicle-to-grid network structure in the smart grid, we propose a location privacy protection model based on distributed differential privacy technology for EVs in vehicle-to-grid networks. We demonstrate application of the proposed model in real spatial data and show that it can achieve the best effect on the security of the algorithm and the availability of data.

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Schade, Michael, Rico Piehler, Claudius Warwitz, and Christoph Burmann. "Increasing consumers’ intention to use location-based advertising." Journal of Product & Brand Management 27, no. 6 (September 17, 2018): 661–69. http://dx.doi.org/10.1108/jpbm-06-2017-1498.

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Purpose This study aims to investigate the influence of advertising value and privacy concerns on consumers’ intention to use location-based advertising. It also explores if brand trust toward location-based advertising providers and consumers’ privacy self-efficacy reduce privacy concerns. Design/methodology/approach Based on the privacy calculus and expectancy theory, a conceptual model is developed and empirically tested through structural equation modeling using cross-sectional data of 1,121 actual smartphone users from Germany. Findings Advertising value positively and privacy concerns negatively affect consumers’ intention to use location-based advertising. As expected, brand trust and consumers’ privacy self-efficacy can reduce consumers’ privacy concerns. Research limitations/implications Further research should test and validate the proposed framework in other cultures to gain insights into the culturally specific relevance of privacy concerns and their antecedents. The current study includes sociodemographics as potential moderators; additional studies could investigate other potential moderators (e.g. personality, values). Practical implications To reduce consumers’ privacy concerns, location-based advertising providers should make their offers transparent and give consumers control, to increase their privacy self-efficacy. They also should work to strengthen their brand, monitor brand trust trends and avoid any trust-damaging behavior. Originality/value This study introduces brand trust toward location-based advertising providers and privacy self-efficacy as factors to reduce consumers’ privacy concerns. It also encompasses a broader, general sample of consumers, which increases the generalizability and practical relevance of the results and supports an initial investigation of sociodemographic factors as potential moderators in this context.

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Kim, Jongwook, and Byungjin Lim. "Effective and Privacy-Preserving Estimation of the Density Distribution of LBS Users under Geo-Indistinguishability." Electronics 12, no. 4 (February 12, 2023): 917. http://dx.doi.org/10.3390/electronics12040917.

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With the widespread use of mobile devices, location-based services (LBSs), which provide useful services adjusted to users’ locations, have become indispensable to our daily lives. However, along with several benefits, LBSs also create problems for users because to use LBSs, users are required to disclose their sensitive location information to the service providers. Hence, several studies have focused on protecting the location privacy of individual users when using LBSs. Geo-indistinguishability (Geo-I), which is based on the well-known differential privacy, has recently emerged as a de-facto privacy definition for the protection of location data in LBSs. However, LBS providers require aggregate statistics, such as user density distribution, for the purpose of improving their service quality, and deriving them accurately from the location dataset received from users is difficult owing to the data perturbation of Geo-I. Thus, in this study, we investigated two different approaches, the expectation-maximization (EM) algorithm and the deep learning based approaches, with the aim of precisely computing the density distribution of LBS users while preserving the privacy of location datasets. The evaluation results show that the deep learning approach significantly outperforms other alternatives at all privacy protection levels. Furthermore, when a low level of privacy protection is sufficient, the approach based on the EM algorithm shows performance results similar to those of the deep learning solution. Thus, it can be used instead of a deep learning approach, particularly when training datasets are not available.

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Xing, Ling, Dexin Zhang, Honghai Wu, Huahong Ma, and Xiaohui Zhang. "Distributed K-Anonymous Location Privacy Protection Algorithm Based on Interest Points and User Social Behavior." Electronics 12, no. 11 (May 29, 2023): 2446. http://dx.doi.org/10.3390/electronics12112446.

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Location-based services have become an important part of our daily lives, and while users enjoy convenient Internet services, they also face the risk of privacy leakage. K-anonymity is a widely used method to protect location privacy, but most existing K-anonymity location privacy protection schemes use virtual locations to construct anonymity zones, which have the problem of being vulnerable to attackers through background knowledge, while the improved collaborative K-anonymity scheme does not sufficiently consider whether collaborating users share similar attributes. We propose a distributed K-anonymity location privacy-preserving algorithm based on interest points and user social behaviors to solve these problems in existing K-anonymity schemes. The method determines the similarity of users by their interest points and social behaviors and then selects users with high similarity to build an anonymous set of collaborative users. Finally, to ensure the relatively uniform distribution of collaborative users, a homogenization algorithm is used to make the anonymous location points as dispersed as possible. The experimental results showed that our algorithm can effectively resist background attacks, and the uniformly distributed anonymous location points can achieve higher-quality anonymous regions.

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Xia, Xiu-Feng, Miao Jiang, Xiang-Yu Liu, and Chuan-Yu Zong. "Location-Visiting Characteristics Based Privacy Protection of Sensitive Relationships." Electronics 11, no. 8 (April 12, 2022): 1214. http://dx.doi.org/10.3390/electronics11081214.

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In the era of Internet of Things (IoT), the problem of the privacy leakage of sensitive relationships is critical. This problem is caused by the spatial–temporal correlation between users in location-based social networks (LBSNs). To solve this problem, a sensitive relationship-protection algorithm based on location-visiting characteristics is proposed in this paper. Firstly, a new model based on location-visiting characteristics is proposed for calculating the similarity between users, which evaluates check-in features of users and locations. In order to avoid an adversary inferring sensitive relationship privacy and to ensure the utility of data, our proposed algorithm adopts a heuristic rule to evaluate the impact of deduction contributions and information loss caused by data modifications. In addition, location-search technology is proposed to improve the algorithm’s execution efficiency. The experimental results show that our proposed algorithm can effectively protect the privacy of sensitive data.

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Xu, Heng. "Locus of Control and Location Privacy: An Empirical Study in Singapore." Journal of Global Information Technology Management 13, no. 3 (July 2010): 63–87. http://dx.doi.org/10.1080/1097198x.2010.10856520.

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Baseri, Yaser, Abdelhakim Hafid, and Soumaya Cherkaoui. "Privacy preserving fine-grained location-based access control for mobile cloud." Computers & Security 73 (March 2018): 249–65. http://dx.doi.org/10.1016/j.cose.2017.10.014.

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Magkos, Emmanouil. "Cryptographic Approaches for Privacy Preservation in Location-Based Services." International Journal of Information Technologies and Systems Approach 4, no. 2 (July 2011): 48–69. http://dx.doi.org/10.4018/jitsa.2011070104.

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Current research in location-based services (LBSs) highlights the importance of cryptographic primitives in privacy preservation for LBSs, and presents solutions that attempt to support the (apparently) mutually exclusive requirements for access control and context privacy (i.e., identity and/or location), while at the same time adopting more conservative assumptions in order to reduce or completely remove the need for trust on system entities (e.g., the LBS provider, the network operator, or other peer nodes). This paper surveys the current state of knowledge concerning the use of cryptographic primitives for privacy-preservation in LBS applications.

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Tošić, Aleksandar, Niki Hrovatin, and Jernej Vičič. "A WSN Framework for Privacy Aware Indoor Location." Applied Sciences 12, no. 6 (March 21, 2022): 3204. http://dx.doi.org/10.3390/app12063204.

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In the past two decades, technological advancements in smart devices, IoT, and smart sensors have paved the way towards numerous implementations of indoor location systems. Indoor location has many important applications in numerous fields, including structural engineering, behavioral studies, health monitoring, etc. However, with the recent COVID-19 pandemic, indoor location systems have gained considerable attention for detecting violations in physical distancing requirements and monitoring restrictions on occupant capacity. However, existing systems that rely on wearable devices, cameras, or sound signal analysis are intrusive and often violate privacy. In this research, we propose a new framework for indoor location. We present an innovative, non-intrusive implementation of indoor location based on wireless sensor networks. Further, we introduce a new protocol for querying and performing computations in wireless sensor networks (WSNs) that preserves sensor network anonymity and obfuscates computation by using onion routing. We also consider the single point of failure (SPOF) of sink nodes in WSNs and substitute them with a blockchain-based application through smart contracts. Our set of smart contracts is able to build the onion data structure and store the results of computation. Finally, a role-based access control contract is used to secure access to the system.

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Zhang, Xueqin, Qianru Zhou, Chunhua Gu, and Liangxiu Han. "The Location Privacy Preserving of Social Network Based on RCCAM Access Control." IETE Technical Review 35, sup1 (September 6, 2018): 68–75. http://dx.doi.org/10.1080/02564602.2018.1507767.

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Kim, Junghwan, and Mei-Po Kwan. "An Examination of People’s Privacy Concerns, Perceptions of Social Benefits, and Acceptance of COVID-19 Mitigation Measures That Harness Location Information: A Comparative Study of the U.S. and South Korea." ISPRS International Journal of Geo-Information 10, no. 1 (January 12, 2021): 25. http://dx.doi.org/10.3390/ijgi10010025.

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This paper examines people’s privacy concerns, perceptions of social benefits, and acceptance of various COVID-19 control measures that harness location information using data collected through an online survey in the U.S. and South Korea. The results indicate that people have higher privacy concerns for methods that use more sensitive and private information. The results also reveal that people’s perceptions of social benefits are low when their privacy concerns are high, indicating a trade-off relationship between privacy concerns and perceived social benefits. Moreover, the acceptance by South Koreans for most mitigation methods is significantly higher than that by people in the U.S. Lastly, the regression results indicate that South Koreans (compared to people in the U.S.) and people with a stronger collectivist orientation tend to have higher acceptance for the control measures because they have lower privacy concerns and perceive greater social benefits for the measures. These findings advance our understanding of the important role of geographic context and culture as well as people’s experiences of the mitigation measures applied to control a previous pandemic.

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Li, Yuxi, Fucai Zhou, Yue Ge, and Zifeng Xu. "Privacy-Enhancing k-Nearest Neighbors Search over Mobile Social Networks." Sensors 21, no. 12 (June 9, 2021): 3994. http://dx.doi.org/10.3390/s21123994.

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Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.

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Thompson, London, and Csilla Farkas. "PRIVACY AND SECURITY FOR TELEHEALTH DEVICES." Innovation in Aging 3, Supplement_1 (November 2019): S836. http://dx.doi.org/10.1093/geroni/igz038.3081.

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Abstract In this research, we study the privacy and security capabilities provided by telehealth devices. Our aim is to evaluate how vulnerable these popular devices are in the presence of malicious cyber attackers. As older adults increasingly rely on telehealth devices, it is crucial that cybersecurity aspects of these devices are clearly communicated to them. Moreover, older adults frequently lack the technical expertise to evaluate the security and privacy capabilities of the devices. The lack of control over telehealth devices is a major concern for older adults. Older adults view certain limitations within these devices as decreasing their privacy and security. These limitations include the lack of control over accepting calls, taking screenshots, and assigning access privileges. For large scale adaptation of telehealth devices by older adults, it is crucial that these devices not only satisfy their intended purpose but also exhibit user friendly features and strong security and privacy capabilities. Modeling cyber threats against telehealth devices is not studied sufficiently . Malicious actors may compromise telehealth devices and create further threats to security and privacy of the users. In this research, we studied the cyber threats against telehealth devices. We built a threat model that ranks cyber threats based on their impact. We investigated how the operating system of popular devices supports access control. We found that none of the current technologies support location-based access control. We claim that this represents a major limitation and that supporting location-based access control is necessary to ensure users’ privacy in their own home.

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Khan, Shawal, Ishita Sharma, Mazzamal Aslam, Muhammad Zahid Khan, and Shahzad Khan. "Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey." Future Internet 13, no. 4 (April 10, 2021): 96. http://dx.doi.org/10.3390/fi13040096.

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A Vehicular Ad-hoc Network (VANET) comprises a group of moving or stationary vehicles connected by a wireless network. VANETs play a vital role in providing safety and comfort to drivers in vehicular environments. They provide smart traffic control and real-time information, event allocation. VANETs have received attention in support of safe driving, intelligent navigation, emergency and entertainment applications in vehicles. Nevertheless, these increasingly linked vehicles pose a range of new safety and security risks to both the host and its associated properties and may even have fatal consequences. Violations of national privacy and vehicle identities are a major obstacle to introducing forced contact protocols in vehicles. Location privacy refers to the privacy of the vehicle (driver) and the location of the vehicle. Whenever a vehicle sends a message, no one but authorized entities should know their real identity and location of the vehicle. All the messages sent by the vehicle must be authenticated before processing, hence location privacy is an important design aspect to be considered in VANETs operations. The novelty of this paper is that it specifically reviews location privacy in VANETs in terms of operational and safety concerns. Furthermore, it presents a critical analysis of various attacks, identity thefts, manipulation and other techniques in vogue for location privacy protection available in state-of-the-art solutions for VANETs. The efforts in this paper will help researchers to develop a great breadth of understanding pertaining to location privacy issues and various security threats encountered by VANETs and present the critical analysis of the available state-of-the- art solutions to maintain location privacy in VANETs.

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Song, Fuyuan, Yiwei Liu, Siyao Ma, Qin Jiang, Xiang Zhang, and Zhangjie Fu. "Enabling Efficient and Privacy-Preserving Task Allocation with Temporal Access Control for Mobile Crowdsensing." Electronics 12, no. 14 (July 10, 2023): 3016. http://dx.doi.org/10.3390/electronics12143016.

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The increasing proliferation of GPS-enabled mobile devices, including Unmanned Aerial Vehicles (UAVs), smartphones, and laptops, has resulted in a significant upsurge in the outsourcing of spatial data to cloud servers for storage and computation purposes, such as task allocation and location-based services. However, the reliance on untrusted cloud servers introduces the risk of privacy breaches, as these servers possess the ability to deduce and access users’ private information based on task content and query requirements. Existing privacy-preserving task-allocation schemes offer only coarse-grained and non-temporal access control, which restricts their applicability in scenarios involving multiple users and time-series data, such as trajectory and time-related routes. To overcome these challenges, this paper proposes an Efficient and Privacy-Preserving Task Allocation with Temporal Access Control (EPTA-T) scheme for mobile crowdsensing. By leveraging the techniques of Gray code and randomizable matrix multiplication, EPTA-T achieves efficient and privacy-preserving task allocation in mobile crowdsensing. Specifically, EPTA-T supports fine-grained and temporal access control through the utilization of an attribute-based access tree and function integration. The formal security analysis demonstrated that EPTA-T effectively guarantees data privacy and query privacy throughout the task allocation process. Extensive experiments conducted using a real-world dataset indicated that the EPTA-T scheme surpassed the performance of the state-of-the-art scheme.

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Cui, Longyin, and Xiwei Wang. "A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System." Electronics 11, no. 7 (April 6, 2022): 1153. http://dx.doi.org/10.3390/electronics11071153.

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Point-of-interest (POI) recommender systems (RSes) have gained significant popularity in recent years due to the prosperity of location-based social networks (LBSN). However, in the interest of personalization services, various sensitive contextual information is collected, causing potential privacy concerns. This paper proposes a cascaded privacy-preserving POI recommendation (CRS) framework that protects contextual information such as user comments and locations. We demonstrate a minimized trade-off between the privacy-preserving feature and prediction accuracy by applying a semi-decentralized model to real-world datasets.

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Zeng, Mengjia, Zhaolin Cheng, Xu Huang, and Bo Zheng. "Spatial Crowdsourcing Quality Control Model Based on K-Anonymity Location Privacy Protection and ELM Spammer Detection." Mobile Information Systems 2019 (February 4, 2019): 1–10. http://dx.doi.org/10.1155/2019/2723686.

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The spatial crowdsourcing task places workers at a risk of privacy leakage. If positional information is not required to submit, it will result in an increased error rate and number of spammers, which together affects the quality of spatial crowdsourcing. In this paper, a spatial crowdsourcing quality control model is proposed, called SCQCM. In the model, the spatial k-anonymity algorithm is used to protect the position privacy of the general spatial crowdsourcing workers. Next, an ELM (extreme learning machine) algorithm is used to detect spammers, while an EM (expectation maximization) algorithm is used to estimate the error rate. Finally, different parameters are selected, and the efficiency of the model is simulated. The results showed that the spatial crowdsourcing model proposed in this paper guaranteed the quality of crowdsourcing projects on the premise of protecting the privacy of workers.

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Arana, O., F. Garcia, and J. Gomez. "Analysis of the effectiveness of transmission power control as a location privacy technique." Computer Networks 163 (November 2019): 106880. http://dx.doi.org/10.1016/j.comnet.2019.106880.

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Li, Zhidan, Wenmin Li, QiaoYan Wen, Jiageng Chen, Wei Yin, and Kaitai Liang. "An efficient blind filter: Location privacy protection and the access control in FinTech." Future Generation Computer Systems 100 (November 2019): 797–810. http://dx.doi.org/10.1016/j.future.2019.04.026.

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Lohan, Elena Simona, Viktoriia Shubina, and Dragoș Niculescu. "Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications." Sensors 22, no. 2 (January 17, 2022): 687. http://dx.doi.org/10.3390/s22020687.

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Future social networks will rely heavily on sensing data collected from users’ mobile and wearable devices. A crucial component of such sensing will be the full or partial access to user’s location data, in order to enable various location-based and proximity-detection-based services. A timely example of such applications is the digital contact tracing in the context of infectious-disease control and management. Other proximity-detection-based applications include social networking, finding nearby friends, optimized shopping, or finding fast a point-of-interest in a commuting hall. Location information can enable a myriad of new services, among which we have proximity-detection services. Addressing efficiently the location privacy threats remains a major challenge in proximity-detection architectures. In this paper, we propose a location-perturbation mechanism in multi-floor buildings which highly protects the user location, while preserving very good proximity-detection capabilities. The proposed mechanism relies on the assumption that the users have full control of their location information and are able to get some floor-map information when entering a building of interest from a remote service provider. In addition, we assume that the devices own the functionality to adjust to the desired level of accuracy at which the users disclose their location to the service provider. Detailed simulation-based results are provided, based on multi-floor building scenarios with hotspot regions, and the tradeoff between privacy and utility is thoroughly investigated.

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Zidani, Ferroudja, Fouzi Semchedine, and Marwane Ayaida. "Estimation of Neighbors Position privacy scheme with an Adaptive Beaconing approach for location privacy in VANETs." Computers & Electrical Engineering 71 (October 2018): 359–71. http://dx.doi.org/10.1016/j.compeleceng.2018.07.040.

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Balaji, T. S., Robert H. Miller, Clyde C. Heppner, and R. Brian Landers. "Privacy Rules: Method and System for Conveying Location-Granularity Preferences with Location-Based Service Requests." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 4 (September 2005): 633–36. http://dx.doi.org/10.1177/154193120504900407.

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The results of an online survey support a growing body of evidence that user discomfort increases in proportion to the accuracy with which wireless communication networks determine the physical location of a wireless device. We describe a method for conveying and applying user preferences for location granularity (inaccuracy) with such location-based services. Using this method, a user may limit the location granularity either globally, for particular services, or for particular individuals who might wish to locate the user. Upon detecting a request to use a location service, a wireless device sends to the service provider a message directing how to carry out the requested service. Upon receiving the instructions, the service provider would proceed to determine the location of the device, adjust the granularity as appropriate to the user's preference, then return the adjusted location. We believe that this method will alleviate some of users' concerns related to the privacy and control of location information.

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Kim, JeongYeon. "Protecting Metadata of Access Indicator and Region of Interests for Image Files." Security and Communication Networks 2020 (January 22, 2020): 1–10. http://dx.doi.org/10.1155/2020/4836109.

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With popularity of social network services, the security and privacy issues over shared contents receive many attentions. Besides, multimedia files have additional concerns of copyright violation or illegal usage to share over communication networks. For image file management, JPEG group develops new image file format to enhance security and privacy features. Adopting a box structure with different application markers, new standards for privacy and security provide a concept of replacement substituting a private part of the original image or metadata with an alternative public data. In this paper, we extend data protection features of new JPEG formats to remote access control as a metadata. By keeping location information of access control data as a metadata in image files, the image owner can allow or deny other’s data consumption regardless where the media file is. License issue also can be resolved by applying new access control schemes, and we present how new formats protect commercial image files against unauthorized accesses.

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Wang, Na, Junsong Fu, Jiwen Zeng, and Bharat K. Bhargava. "Source-location privacy full protection in wireless sensor networks." Information Sciences 444 (May 2018): 105–21. http://dx.doi.org/10.1016/j.ins.2018.02.064.

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Narain, Sashank, and Guevara Noubir. "Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing." Proceedings on Privacy Enhancing Technologies 2019, no. 2 (April 1, 2019): 66–87. http://dx.doi.org/10.2478/popets-2019-0020.

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Abstract We present the design, implementation and evaluation of a system, called MATRIX, developed to protect the privacy of mobile device users from location inference and sensor side-channel attacks. MATRIX gives users control and visibility over location and sensor (e.g., Accelerometers and Gyroscopes) accesses by mobile apps. It implements a PrivoScope service that audits all location and sensor accesses by apps on the device and generates real-time notifications and graphs for visualizing these accesses; and a Synthetic Location service to enable users to provide obfuscated or synthetic location trajectories or sensor traces to apps they find useful, but do not trust with their private information. The services are designed to be extensible and easy for users, hiding all of the underlying complexity from them. MATRIX also implements a Location Provider component that generates realistic privacy-preserving synthetic identities and trajectories for users by incorporating traffic information using historical data from Google Maps Directions API, and accelerations using statistical information from user driving experiments. These mobility patterns are generated by modeling/solving user schedule using a randomized linear program and modeling/solving for user driving behavior using a quadratic program. We extensively evaluated MATRIX using user studies, popular location-driven apps and machine learning techniques, and demonstrate that it is portable to most Android devices globally, is reliable, has low-overhead, and generates synthetic trajectories that are difficult to differentiate from real mobility trajectories by an adversary.

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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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Yang, Jianxi, Manoranjan Dash, and Sin G. Teo. "PPTPF: Privacy-Preserving Trajectory Publication Framework for CDR Mobile Trajectories." ISPRS International Journal of Geo-Information 10, no. 4 (April 6, 2021): 224. http://dx.doi.org/10.3390/ijgi10040224.

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As mobile phone technology evolves quickly, people could use mobile phones to conduct business, watch entertainment shows, order food, and many more. These location-based services (LBS) require users’ mobility data (trajectories) in order to provide many useful services. Latent patterns and behavior that are hidden in trajectory data should be extracted and analyzed to improve location-based services including routing, recommendation, urban planning, traffic control, etc. While LBSs offer relevant information to mobile users based on their locations, revealing such areas can pose user privacy violation problems. An efficient privacy preservation algorithm for trajectory data must have two characteristics: utility and privacy, i.e., the anonymized trajectories must have sufficient utility for the LBSs to carry out their services, and privacy must be intact without any compromise. Literature on this topic shows many methods catering to trajectories based on GPS data. In this paper, we propose a privacy preserving method for trajectory data based on Call Detail Record (CDR) information. This is useful as a vast number of people, particularly in underdeveloped and developing places, either do not have GPS-enabled phones or do not use them. We propose a novel framework called Privacy-Preserving Trajectory Publication Framework for CDR (PPTPF) for moving object trajectories to address these concerns. Salient features of PPTPF include: (a) a novel stay-region based anonymization technique that caters to important locations of a user; (b) it is based on Spark, thus it can process and anonymize a significant volume of trajectory data successfully and efficiently without affecting LBSs operations; (c) it is a component-based architecture where each component can be easily extended and modified by different parties.

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Zou, Shihong, Jinwen Xi, Honggang Wang, and Guoai Xu. "CrowdBLPS: A Blockchain-Based Location-Privacy-Preserving Mobile Crowdsensing System." IEEE Transactions on Industrial Informatics 16, no. 6 (June 2020): 4206–18. http://dx.doi.org/10.1109/tii.2019.2957791.

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Boudjit, Saadi, Benamar Kadri, and Leila Benarous. "Camouflage-based location privacy preserving scheme in vehicular ad hoc networks." International Journal of Vehicle Information and Communication Systems 8, no. 3 (2023): 217–36. http://dx.doi.org/10.1504/ijvics.2023.10058397.

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Benarous, Leila, Benamar Kadri, and Saadi Boudjit. "Camouflage-based location privacy preserving scheme in vehicular ad hoc networks." International Journal of Vehicle Information and Communication Systems 8, no. 3 (2023): 217–36. http://dx.doi.org/10.1504/ijvics.2023.132926.

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Zeng, Wen, Reem Bashir, Trevor Wood, Francois Siewe, Helge Janicke, and Isabel Wagner. "How Location-Aware Access Control Affects User Privacy and Security in Cloud Computing Systems." EAI Endorsed Transactions on Cloud Systems 6, no. 18 (September 7, 2020): 165236. http://dx.doi.org/10.4108/eai.13-7-2018.165236.

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Huang, Jianwei, Mei-Po Kwan, and Junghwan Kim. "How Culture and Sociopolitical Tensions Might Influence People’s Acceptance of COVID-19 Control Measures That Use Individual-Level Georeferenced Data." ISPRS International Journal of Geo-Information 10, no. 7 (July 20, 2021): 490. http://dx.doi.org/10.3390/ijgi10070490.

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This study extends an earlier study in the United States and South Korea on people’s privacy concerns for and acceptance of COVID-19 control measures that use individual-level georeferenced data (IGD). Using a new dataset collected via an online survey in Hong Kong, we first examine the influence of culture and recent sociopolitical tensions on people’s privacy concerns for and acceptance of three types of COVID-19 control measures that use IGD: contact tracing, self-quarantine monitoring, and location disclosure. We then compare Hong Kong people’s views with the views of people in the United States and South Korea using the pooled data of the three study areas. The results indicate that, when compared to people in the United States and South Korea, people in Hong Kong have a lower acceptance rate for digital contact tracing and higher acceptance rates for self-quarantine monitoring using e-wristbands and location disclosure. Further, there is geographic heterogeneity in the age and gender differences in privacy concerns, perceived social benefits, and acceptance of COVID-19 control measures: young people (age < 24) and women in Hong Kong and South Korea have greater privacy concerns than men. Further, age and gender differences in privacy concerns, perceived social benefits, and acceptance of COVID-19 control measures in Hong Kong and South Korea are larger than those in the United States, and people in Hong Kong have the largest age and gender differences in privacy concerns, perceived social benefits, and acceptance of COVID-19 measures among the three study areas.

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He, Peicong, Yang Xin, Bochuan Hou, and Yixian Yang. "PKGS: A Privacy-Preserving Hitchhiking Task Assignment Scheme for Spatial Crowdsourcing." Electronics 12, no. 15 (August 2, 2023): 3318. http://dx.doi.org/10.3390/electronics12153318.

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Privacy-preserving task assignment is vital to assign a task to appropriate workers and protect workers’ privacy or task privacy for spatial crowdsourcing (SC). Existing solutions usually require each worker to travel to the task location on purpose to perform this task, which fails to consider that workers have specific trajectories and carry out the task on their way in a hitchhiking manner. To this end, this paper proposes a privacy-preserving hitchhiking task assignment scheme for SC, named PKGS. Specifically, we formulate the privacy-preserving hitchhiking task assignment as a decision problem of the relationship between dot and line under privacy protection. In particular, we present a privacy-preserving travel distance calculation protocol and a privacy-preserving comparison protocol through the Paillier cryptosystem and the SC framework. Results of theoretical analysis and experimental evaluation show that PKGS can not only protect the location privacy of both each worker and the task simultaneously but also assign the task to the worker holding a minimum travel distance. In contrast to prior solutions, PKGS outperforms in the computation of travel distance and task assignment.

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Takbiri, Nazanin, Virat Shejwalkar, Amir Houmansadr, Dennis L. Goeckel, and Hossein Pishro-Nik. "Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms." IEEE Wireless Communications Letters 9, no. 11 (November 2020): 2005–9. http://dx.doi.org/10.1109/lwc.2020.3011361.

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Li, Weihao, Chen Li, and Yeli Geng. "APS: Attribute-aware privacy-preserving scheme in location-based services." Information Sciences 527 (July 2020): 460–76. http://dx.doi.org/10.1016/j.ins.2019.02.025.

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Didouh, Ahmed, Yassin El Hillali, Atika Rivenq, and Houda Labiod. "Novel Centralized Pseudonym Changing Scheme for Location Privacy in V2X Communication." Energies 15, no. 3 (January 18, 2022): 692. http://dx.doi.org/10.3390/en15030692.

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Vehicular ad hoc networks allow vehicles to share their information for the safety and efficiency of traffic purposes. However, information sharing can threaten the driver’s privacy as it includes spatiotemporal information, and the messages are unencrypted and broadcasted periodically. Therefore, they cannot estimate their privacy level because it also depends on their surroundings. This article proposes a centralized adaptive pseudonym change scheme that permits the certificate’s authority to adjust the pseudonyms assignment for each requesting vehicle. This scheme adapts dynamically depending on the density of the traffic environment and the user’s privacy level, and it aims to solve the trade-off problem between wasting pseudonyms and Sybil attack. We employ a Knapsack problem-based algorithm for target tracking and an entropy-based method to measure each vehicle’s privacy. In order to demonstrate the applicability of our framework, we use real-life data captured during the interoperability tests of the European project InterCor. According to the experimental results, the proposed scheme could easily estimate the level of confidentiality and, therefore, may best respond to the adaptation of the pseudonyms.

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Wang, Gui Chao, Ai Li Zhang, and Yong Zhen Li. "Secure Authentication Protocol of RFID System Based on Access Control." Advanced Engineering Forum 6-7 (September 2012): 1072–77. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.1072.

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The security and privacy problem of low-cost RFID system is one of the most difficult conundrums in the RFID research field. A protocol based on access control was proposed in this paper, which used the reader access, partial ID, XOR operation, etc. By using of the reader authority distribution method, the unauthorized tag`s information was prevented give-away and it can avoid the lawful reader attack, location privacy attack, etc. Function of the reader was fully used. At the same time, the back-end database`s load and the time of the tag`s answer were reduced. Compared with several traditional security authentication protocols, this protocol is more security, lower energy consumption and more suitable for low-cost RFID system.

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Journal articles on the topic 'Location- Privacy control'

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