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PISA: A proximity-based social networking (PBSN) protection model

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Abstract

The widespread adoption of Proximity-based Social Networking (PBSN) applications has been accompanied with several privacy concerns involving location information. As a result, many studies were directed towards innovative privacy-preserving solutions to provide a secure platform for mobile users. Despite the success of these solutions, there is a research gap in terms of the evaluation and analysis of their protection features. An in-depth evaluation of the privacy and security provisions in PBSN systems is necessary to assess the protection properties. In this paper, a comprehensive protection assessment model, the PISA model, is proposed to evaluate the privacy and security features of PBSN frameworks. The main objectives of this study refer to defining the protection goals of PBSN systems by reviewing the privacy and security requirements, analyzing the associated location privacy threats, and formulating the PISA model based on the quantification of the related protection goals using the privacy metrics. The study adopts an exploratory research methodology and explores four distinct research questions. The PISA model enables an extensive evaluation of privacy-preserving PBSN frameworks concerning their privacy and security features which can be further useful for researchers during the development of privacy-preserving algorithms to prevent flaws in advance and improve where necessary. Future works of the current research can focus on the analysis of privacy policies and adversary models based on their assumptions, resources, and capabilities.

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References

  • Al-Badawy, A.M., H.M. Abbas, and M. Belal. 2018. A TTP-Free Location Privacy Framework for Mobile Social Networks with Key Agreement Protocol. International Journal of Applied Engineering Research 13 (14): 11540–11547.

    Google Scholar 

  • Al-Dhubhani, R.S., Cazalas, J., Mehmood, R., Katib, I. and Saeed, F. 2019. A Framework for Preserving Location Privacy for Continuous Queries. In: International Conference of Reliable Information and Communication Technology, Johor, Malaysia, pp. 819–832. Springer, Cham, 22–23 September 2019.

  • Amar, Y., Haddadi, H. and Mortier, R. 2018. An Information-Theoretic Approach to Time-Series Data Privacy. In: Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems, Porto Portugal, pp. 1–6. EuroSys, 23–26 April 2018.

  • Arseni, S.C., Halunga, S., Fratu, O., Vulpe, A. and Suciu, G. 2015. Analysis of The Security Solutions Implemented In Current Internet of Things Platforms. In: 2015 Conference Grid, Cloud & High-Performance Computing in Science (ROLCG), Cluj-Napoca, Romania, pp. 1–4. IEEE, 28–30 October 2015.

  • Babar, S., Mahalle, P., Stango, A., Prasad, N. and Prasad, R. 2010 Proposed Security Model and Threat Taxonomy for the Internet of Things (IoT). In: Third International Conference on Recent Trends in Network Security and Applications, Chennai, India, pp. 420–429. Springer, 23–25 July.

  • Bernabe, J.B., Hernández, J.L., Moreno, M.V. and Gomez, A.F.S. 2014. Privacy-Preserving Security Framework for a Social-Aware Internet of Things. In: International conference on Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services, Belfast, United Kingdom, pp. 408–415. Springer, Cham, 2–5 December 2014.

  • Buchanan, W.J., Z. Kwecka, and E. Ekonomou. 2013. A Privacy Preserving Method Using Privacy Enhancing Techniques for Location Based Services. Mobile Networks and Applications 18 (5): 728–737.

    Article  Google Scholar 

  • Chamarajnagar, R. and Ashok, A. 2019. Privacy Invasion through Smarthome IoT Sensing. In: 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Boston, MA, USA, pp. 1–9. IEEE, 10–13 June 2019.

  • Do, Q., B. Martini, and K.K.R. Choo. 2019. The Role of the Adversary Model in Applied Security Research. Computers & Security 81: 156–181.

    Article  Google Scholar 

  • Jiang, H., J. Li, P. Zhao, F. Zeng, Z. Xiao, and A. Iyengar. 2021. Location Privacy-Preserving Mechanisms in Location-Based Services: A Comprehensive Survey. ACM Computing Surveys (CSUR) 54 (1): 1–36.

    Google Scholar 

  • Kim, T., I.R. Chen, Y. Lin, A.Y.Y. Wang, J.Y.H. Yang, and P. Yang. 2019. Impact of Similarity Metrics on Single-Cell RNA-Seq Data Clustering. Briefings in Bioinformatics 20 (6): 2316–2326.

    Article  Google Scholar 

  • Kong, L., Z. Liu, and Y. Huang. 2014. Spot: Locating Social Media Users Based on Social Network Context. Proceedings of the VLDB Endowment 7 (13): 1681–1684.

    Article  Google Scholar 

  • Lee, C., Y. Guo, and L. Yin. 2013. A Framework of Evaluation Location Privacy in Mobile Network. Procedia Computer Science 17: 879–887.

    Article  Google Scholar 

  • Li, H., H. Zhu, S. Du, X. Liang, and X. Shen. 2016. Privacy Leakage of Location Sharing In Mobile Social Networks: Attacks and Defense. IEEE Transactions on Dependable and Secure Computing 15 (4): 646–660.

    Article  Google Scholar 

  • Li, M., Cao, N., Yu, S. and Lou, W. 2011. Findu: Privacy-Preserving Personal Profile Matching In Mobile Social Networks. In: 2011 Proceedings IEEE INFOCOM, Shanghai, China, pp. 2435–2443. IEEE. 10–15 April 2011.

  • Lilien, L., and B. Bhargava. 2006. A Scheme for Privacy-Preserving Data Dissemination. IEEE Transactions on Systems, Man, and Cybernetics-Part A 36 (3): 503–506.

    Article  Google Scholar 

  • Liu, L. 2009. Privacy and Location Anonymization in Location-Based Services. SIGSPATIAL Special 1 (2): 15–22.

    Article  Google Scholar 

  • Lu, R., Lin, X., Shi, Z. and Shao, J. 2014. PLAM: A Privacy-Preserving Framework for Local-Area Mobile Social Networks. In: INFOCOM 2014-IEEE Conference on Computer Communications, Toronto, ON, Canada, pp. 763–771. IEEE, 27 April-2 May 2014.

  • Ma, X., H. Li, J. Ma, Q. Jiang, S. Gao, N. Xi, and D. Lu. 2017. APPLET: A Privacy-Preserving Framework for Location-Aware Recommender System. Science China Information Sciences 60 (9): 1–16.

    Article  Google Scholar 

  • Mamonov, S., and R. Benbunan-Fich. 2018. The Impact of Information Security Threat Awareness on Privacy-Protective Behaviors. Computers in Human Behavior 83 (3): 32–44.

    Article  Google Scholar 

  • Pew Research Center. 2018. Teens, Social Media & Technology Overview 2015. Available at: http://www.pewinternet.org/2015/04/09/teens-social-media-technology-2015/ Accessed 30 Aug. 2021.

  • Puttaswamy, K.P. and Zhao, B.Y. 2010. Preserving Privacy in Location-Based Mobile Social Applications. In: Proceedings of the 11th Workshop on Mobile Computing Systems & Applications, Annapolis, MD, USA, pp. 1–6. ACM, February 2010.

  • Raschke, R.L., A.S. Krishen, and P. Kachroo. 2014. Understanding the Components of Information Privacy Threats for Location-Based Services. Journal of Information Systems 28 (1): 227–242.

    Article  Google Scholar 

  • Ravi, L., V. Subramaniyaswamy, M. Devarajan, K.S. Ravichandran, S. Arunkumar, V. Indragandhi, and V. Vijayakumar. 2019. SECRECSY: A Secure Framework for Enhanced Privacy-Preserving Location Recommendations in Cloud Environment. Wireless Personal Communications 108 (3): 1869–1907.

    Article  Google Scholar 

  • Shao, J., Lu, R. and Lin, X. 2014. FINE: A Fine-Grained Privacy-Preserving Location-Based Service Framework for Mobile Devices. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications, Toronto, ON, Canada, pp. 244–252. IEEE, 27 April-2 May 2014.

  • Shokri, R., Theodorakopoulos, G., Le Boudec, J.Y. and Hubaux, J.P. 2011 Quantifying location privacy. In: 2011 IEEE symposium on security and privacy, Berkeley, CA, USA, pp. 247–262. IEEE, 22–25 May 2011.

  • Shokri, R., Troncoso, C., Diaz, C., Freudiger, J. and Hubaux, J.P. 2010. Unraveling an Old Cloak: K-Anonymity for Location Privacy. In: Proceedings of the 9th annual ACM workshop on Privacy in the electronic society, Chicago Illinois USA, pp. 115–118. CCS, 4 October 2010.

  • Solove, D.J. 2005. A Taxonomy of Privacy. The University of Pennsylvania Law Review 154 (3): 447–564.

    Google Scholar 

  • Song, D., J. Sim, K. Park, and M. Song. 2015. A privacy-preserving continuous location monitoring system for location-based services. International Journal of Distributed Sensor Networks 11: 8.

    Article  Google Scholar 

  • Sun, G., D. Liao, H. Li, H. Yu, and V. Chang. 2016. L2P2: A location-label based approach for privacy preserving in LBS. Future Generation Computer Systems 74: 375–384.

    Article  Google Scholar 

  • Sun, R. and Xue, M. 2020. Quality Assessment of Online Automated Privacy Policy Generators: An Empirical Study. In Proceedings of the Evaluation and Assessment in Software Engineering, Trondheim, Norway, pp. 270–275. ICPS Proceedings, 15–17 April 2020.

  • Thomas, K., Bandara, A.K., Price, B.A. and Nuseibeh, B. 2014. Distilling Privacy Requirements for Mobile Applications. In: Proceedings of the 36th international conference on software engineering, Hyderabad, India, pp. 871–882. ICSE, 31 May 2014- 7 June 2014.

  • Thuiller, W., M. Guéguen, J. Renaud, D.N. Karger, and N.E. Zimmermann. 2019. Uncertainty in Ensembles of Global Biodiversity Scenarios. Nature Communications 10 (1): 1–9.

    Article  Google Scholar 

  • Vu, K., Zheng, R. and Gao, J. 2012. Efficient algorithms for k-anonymous location privacy in participatory sensing. In: 2012 Proceedings IEEE INFOCOM, Orlando, FL, USA, pp. 2399–2407. IEEE, 25–30 March 2012.

  • Wagner, I. 2015. Genomic Privacy Metrics: A systematic Comparison. In: 2015 IEEE Security and Privacy Workshops, San Jose, CA, USA, pp. 50–59. IEEE, 21–22 May 2015.

  • Wagner, I., and D. Eckhoff. 2018. Technical Privacy Metrics: A Systematic Survey. ACM Computing Surveys (CSUR) 51 (3): 1–38.

    Article  Google Scholar 

  • Werner, M. 2016. Privacy-Protected Communication for Location-Based Services. Security and Communication Networks 9 (2): 130–138.

    Article  Google Scholar 

  • Xue, M., Y. Liu, K.W. Ross, and H. Qian. 2016. Thwarting Location Privacy Protection in Location-Based Social Discovery Services. Security and Communication Networks 9 (11): 1496–1508.

    Article  Google Scholar 

  • Yang, D., Zhang, D., Qu, B. and Cudré-Mauroux, P. 2016. PrivCheck: privacy-preserving check-in data publishing for personalized location based services. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, New York, USA, pp. 545–556. ACM, September 2016.

  • Zhao, Y., and I. Wagner. 2020. Using Metrics Suites to Improve the Measurement of Privacy in Graphs. IEEE Transactions on Dependable and Secure Computing 19 (1): 259–274.

    Article  Google Scholar 

  • Zhou, X. 2011. Privacy and Security Assessment of Biometric Template Protection. Doctoral dissertation, Technische Universitt Darmstadt, Germany.

  • Zhu, X., E. Ayday, and R. Vitenberg. 2019. A Privacy-Preserving Framework for Outsourcing Location-Based Services to The Cloud. IEEE Transactions on Dependable and Secure Computing 18 (1): 384–399.

    Article  Google Scholar 

  • Zhu, Z., Cao, G. 2011. Applaus: A Privacy-Preserving Location Proof Updating System for Location-Based Services. In: 2011 Proceedings IEEE INFOCOM, Shanghai, China, pp. 1889–1897. IEEE, 10–15 April 2011.

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Correspondence to Asslinah Mocktoolah Ramtohul.

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Ramtohul, A.M., Khedo, K.K. PISA: A proximity-based social networking (PBSN) protection model. Secur J 36, 165–200 (2023). https://doi.org/10.1057/s41284-022-00334-5

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