Augusto C. S. A. Domingues
Heitor S. Ramos
ABSTRACT
In many different contexts, the encounter between two or more individuals opens a window in which information can be exchanged. Considering Mobile Ad hoc Networks (MANETs) scenarios, encounters - also called contacts - are used to transfer data between nodes (the users). In more recent cases, tracing contacts between individuals has shown to be a strong strategy in mapping the transmission of contagious diseases, such as COVID-19. However, sharing contact data can impose threats to the safety of participants regarding their social and mobility behavior. As an example, we can infer acquaintances, as well as home and work locations. This work presents a strategy to anonymize contact tracing data by utilizing mix-zones, a well-defined concept to anonymize data in a given region. Called social mix-zones, it considers the number of contacts happening in a location, producing anonymized data and protecting the personal integrity of the individuals. We validate the proposal using two real contact tracing data, showing that social mix-zones can cover a large portion of contacts, reducing the risk of malicious location attacks.
- James Bell, David Butler, Chris Hicks, and Jon Crowcroft. 2020. Tracesecure: Towards privacy preserving contact tracing. arXiv preprint arXiv:2004.04059 .Google Scholar
- Alastair R Beresford and Frank Stajano. 2003. Location privacy in pervasive computing . IEEE Pervasive computing , Vol. 2, 1, 46--55. Google ScholarDigital Library
- Alastair R Beresford and Frank Stajano. 2004. Mix zones: User privacy in location-aware services. In Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second IEEE Annual Conference on. IEEE, 127--131. Google ScholarDigital Library
- Antoine Boutet, Sonia Ben Mokhtar, and Vincent Primault. 2016. Uniqueness assessment of human mobility on multi-sensor datasets . Ph.D. Dissertation. LIRIS UMR CNRS 5205.Google Scholar
- Claude Castelluccia, Nataliia Bielova, Antoine Boutet, Mathieu Cunche, Cédric Lauradoux, Daniel Le Métayer, and Vincent Roca. 2020. ROBERT: ROBust and privacy-presERving proximity Tracing .Google Scholar
- David L Chaum. 1981. Untraceable electronic mail, return addresses, and digital pseudonyms. Commun. ACM , Vol. 24, 2, 84--90. Google ScholarDigital Library
- Zhenyu Chen, Yanyan Fu, Min Zhang, Zhenfeng Zhang, and Hao Li. 2018. A Flexible Mix-Zone Selection Scheme Towards Trajectory Privacy Protection. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). IEEE, 1180--1186.Google Scholar
- Chi-Yin Chow and Mohamed F Mokbel. 2011. Trajectory privacy in location-based services and data publication. ACM SigKDD Exp. Newsletter , Vol. 13, 1, 19--29. Google ScholarDigital Library
- Aaqib Bashir Dar, Auqib Hamid Lone, Saniya Zahoor, Afshan Amin Khan, and Roohie Naaz. 2020. Applicability of mobile contact tracing in fighting pandemic (COVID-19): Issues, challenges and solutions. Computer Science Review , 100307.Google Scholar
- Ekler P de Mattos, Augusto CSA Domingues, and Antonio AF Loureiro. 2019. Give me two points and i'll tell you who you are. In 2019 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 1081--1087.Google ScholarCross Ref
- Pedro OS Vaz de Melo, Aline Carneiro Viana, Marco Fiore, Katia Jaffrès-Runser, Frédéric Le Mouël, Antonio AF Loureiro, Lavanya Addepalli, and Chen Guangshuo. 2015. Recast: Telling apart social and random relationships in dynamic networks . Performance Evaluation , Vol. 87, 19--36. Google ScholarDigital Library
- Yves-Alexandre De Montjoye, César A Hidalgo, Michel Verleysen, and Vincent D Blondel. 2013. Unique in the crowd: The privacy bounds of human mobility. Scientific reports , Vol. 3, 1, 1--5.Google Scholar
- Augusto CSA Domingues, Henrique de Souza Santana, Fabr'icio A Silva, Pedro OS Vaz de Melo, and Antonio AF Loureiro. 2019. Are We Still Friends? Evaluating Tie Persistence in Mobility Traces. In Proceedings of the 17th ACM International Symposium on Mobility Management and Wireless Access . 1--8. Google ScholarDigital Library
- Rohan Iyer, Regina Rex, Kevin P McPherson, Darshan Gandhi, Aryan Mahindra, Abhishek Singh, and Ramesh Raskar. 2021. Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geospatial Technologies. arXiv preprint arXiv:2101.02556 .Google Scholar
- David Kotz, Tristan Henderson, Ilya Abyzov, and Jihwang Yeo. 2009. CRAWDAD dataset dartmouth/campus (v. 2009-09-09). Downloaded from https://crawdad.org/dartmouth/campus/20090909 . https://doi.org/10.15783/C7F59TGoogle Scholar
- Luca Pappalardo, Filippo Simini, Gianni Barlacchi, and Roberto Pellungrini. 2019. scikit-mobility: A Python library for the analysis, generation and risk assessment of mobility data . arXiv preprint arXiv:1907.07062 .Google Scholar
- Roberto Pellungrini, Luca Pappalardo, Francesca Pratesi, and Anna Monreale. 2017. A data mining approach to assess privacy risk in human mobility data . ACM Transactions on Intelligent Systems and Technology (TIST) , Vol. 9, 3, 1--27. Google ScholarDigital Library
- Roberto Pellungrini, Luca Pappalardo, Francesca Pratesi, and Anna Monreale. 2018. Analyzing Privacy Risk in Human Mobility Data . In Federation of Int. Conf. on Software Technologies: Applications and Foundations. Springer, 114--129.Google ScholarCross Ref
- Luciana Pelusi, Andrea Passarella, and Marco Conti. 2006. Opportunistic networking: data forwarding in disconnected mobile ad hoc networks. IEEE communications Magazine , Vol. 44, 11, 134--141. Google ScholarDigital Library
- Meng Shen, Yaqian Wei, and Tong Li. 2020. Bluetooth-based COVID-19 Proximity Tracing Proposals: An Overview . arXiv preprint arXiv:2008.12469 .Google Scholar
- Viktoriia Shubina, Sylvia Holcer, Michael Gould, and Elena Simona Lohan. 2020. Survey of decentralized solutions with mobile devices for user location tracking, proximity detection, and contact tracing in the covid-19 era. Data , Vol. 5, 4, 87.Google ScholarCross Ref
- Thiago H Silva, Pedro OS Vaz de Melo, Jussara M Almeida, Juliana Salles, and Antonio AF Loureiro. 2013. A comparison of foursquare and instagram to the study of city dynamics and urban social behavior. In Proceedings of the 2nd ACM SIGKDD international workshop on urban computing. 1--8. Google ScholarDigital Library
- Ruoxi Sun, Wei Wang, Minhui Xue, Gareth Tyson, Seyit Camtepe, and Damith Ranasinghe. 2020. Vetting security and privacy of global covid-19 contact tracing applications. arXiv preprint arXiv:2006.10933 .Google Scholar
- TraceTogether. 2021. TraceTogether, safer together. https://www.tracetogether.gov.sg/ [Online; accessed 04-June-2021].Google Scholar
- Carmela Troncoso, Mathias Payer, Jean-Pierre Hubaux, Marcel Salathé, James Larus, Edouard Bugnion, Wouter Lueks, Theresa Stadler, Apostolos Pyrgelis, Daniele Antonioli, et almbox. 2020. Decentralized privacy-preserving proximity tracing. arXiv preprint arXiv:2005.12273 .Google Scholar
- Tzu-Chieh Tsai and Ho-Hsiang Chan. 2015. NCCU Trace: Social-network-aware mobility trace. IEEE Communications Magazine , Vol. 53, 10, 144--149.Google ScholarDigital Library
- Serge Vaudenay. 2020. Analysis of DP3T . https://eprint.iacr.org/2020/399 [Online; accessed 18-June-2020].Google Scholar
- Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th international conference on World wide web. 791--800. Google ScholarDigital Library
Index Terms
- Social Mix-zones: Anonymizing Personal Information on Contact Tracing Data
Recommendations
Behind the Mix-Zones Scenes: On the Evaluation of the Anonymization Quality
PE-WASUN '22: Proceedings of the 19th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous NetworksIn the flowering of ubiquitous computing, networks like the Internet of Things and the Internet of Vehicles have contributed to connecting objects and sharing location services in broad environments like smart cities bringing many benefits to citizens. ...
Protect your Data and I'll Show Its Utility: A Practical View about Mix-zones Impacts on Mobility Data for Smart City Applications
PE-WASUN '23: Proceedings of the Int'l ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous NetworksWhen designing smart cities' building blocks, mobility data plays a fundamental role in applications and services. However, mobility data usually comes with unrestricted location of its corresponding entities (e.g., citizens and vehicles) and poses ...
Formal analysis of privacy for vehicular mix-zones
ESORICS'10: Proceedings of the 15th European conference on Research in computer securitySafety critical applications for recently proposed vehicle to vehicle ad-hoc networks (VANETs) rely on a beacon signal, which poses a threat to privacy since it could allow a vehicle to be tracked. Mix-zones, where vehicles encrypt their transmissions ...
Comments