HMC

Author:

Maouche Mohamed1,Ben Mokhtar Sonia1,Bouchenak Sara1

Affiliation:

1. Universite de Lyon, CNRS. INSA Lyon, LIRIS, France

Abstract

With the wide propagation of handheld devices, more and more mobile sensors are being used by end users on a daily basis. Those sensors could be leveraged to gather useful mobility data for city planners, business analysts and researches. However, gathering and exploiting mobility data raises many privacy threats. Sensitive information such as one's home or work place, hobbies, religious beliefs, political or sexual preferences can be inferred from the gathered data. In the last decade, Location Privacy Protection Mechanisms (LPPMs) have been proposed to protect user data privacy. However existing LPPMs fail at effectively protecting the users as most of them reason on local mobility features: micro-mobility (e.g., individual geographical coordinates) while ignoring higher level mobility features, which may allow attackers to discriminate between users. In this paper we propose HMC the first LPPM that reasons on the overall user mobility abstracted using heat maps. We evaluate HMC using four real mobility traces and multiple privacy and utility metrics. The results show that with HMC, across all the datasets 87% of mobile users are successfully protected against re-identification attacks, while others LPPMs only achieve a protection ranging from 43% to 79%. By considering only users protected with a high utility, the proportion of users stays high for HMC with 75%, while for others LPPMs it goes down to proportions between 4% and 43%.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Resisting TUL attack: balancing data privacy and utility on trajectory via collaborative adversarial learning;GeoInformatica;2023-10-21

2. Privacy protection control for mobile apps users;Control Engineering Practice;2023-05

3. Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities;Applied Sciences;2023-03-16

4. EDEN;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2021-06-23

5. Empowering mobile crowdsourcing apps with user privacy control;Journal of Parallel and Distributed Computing;2021-01

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