Information about people's movements and the locations they visit enables an increasing number of mobility analytics applications, e.g., in the context of urban and transportation planning, In this setting, rather than collecting or sharing raw data, entities often use aggregation as a privacy protection mechanism, aiming to hide individual users' location traces. Furthermore, to bound information leakage from the aggregates, they can perturb the input of the aggregation or its output to ensure that these are differentially private. In this paper, we set to evaluate the impact of releasing aggregate location time-series on the privacy of individuals contributing to the aggregation. We introduce a framework allowing us to reason about privacy against an adversary attempting to predict users' locations or recover their mobility patterns. We formalize these attacks as inference problems, and discuss a few strategies to model the adversary's prior knowledge based on the information she may have access to. We then use the framework to quantify the privacy loss stemming from aggregate location data, with and without the protection of differential privacy, using two real-world mobility datasets. We find that aggregates do leak information about individuals' punctual locations and mobility profiles. The density of the observations, as well as timing, play important roles, e.g., regular patterns during peak hours are better protected than sporadic movements. Finally, our evaluation shows that both output and input perturbation offer little additional protection, unless they introduce large amounts of noise ultimately destroying the utility of the data.
Attacks on Location Privacy. Prior work presenting attacks on location privacy mostly focuses on inferring users' whereabouts from access to individuals' location data, whether obfuscated or not. Some show that both anonymization and k-anonymity-based mechanisms are ineffective at protecting privacy @cite_29 @cite_35 @cite_6 @cite_38 @cite_5 . (Also see surveys by Krumm @cite_7 and Ghinita @cite_21 ). More recently, researchers analyzed the protection provided by location proximity schemes adopted by social networks @cite_14 @cite_15 @cite_13 , confirming that mechanisms like cloaking or naive perturbation are also unsuccessful.
@cite_35 @cite_38 @cite_14 @cite_7 @cite_29 @cite_21 @cite_6 @cite_5 @cite_15 @cite_13
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There is a rich collection of literature that aims at protecting the privacy of users querying location-based services. One of the most popular location privacy techniques consists in cloaking users' locations such that k users appear as potential senders of a query, thus achieving k-anonymity. This paper analyzes the effectiveness of k-anonymity approaches for protecting location privacy in the presence of various types of adversaries. The unraveling of the scheme unfolds the inconsistency between its components, mainly the cloaking mechanism and the k-anonymity metric. We show that constructing cloaking regions based on the users' locations does not reliably relate to location privacy, and argue that this technique may even be detrimental to users' location privacy. The uncovered flaws imply that existing k-anonymity scheme is a tattered cloak for protecting location privacy. Mobile devices with positioning capabilities allow users to participate in novel and exciting location-based applications. For instance, users may track the whereabouts of their acquaintances in location-aware social networking applications, e.g., GoogleLatitude. Furthermore, users can request information about landmarks in their proximity. Such scenarios require users to report their coordinates to other parties, which may not be fully trusted. Reporting precise locations may result in serious privacy violations, such as disclosure of lifestyle details, sexual orientation, etc. A typical approach to preserve location privacy is to generate a cloaking region (CR) that encloses the user position. However, if locations are continuously reported, an attacker can correlate CRs from multiple timestamps to accurately pinpoint the user position within a CR. In this work, we protect against linkage attacks that infer exact locations based on prior knowledge about maximum user velocity. Assume user u who reports two consecutive cloaked regions A and B. We consider two distinct protection scenarios: in the first case, the attacker does not have information about the sensitive locations on the map, and the objective is to ensure that u can reach some point in B from any point in A. In the second case, the attacker knows the placement of sensitive locations, and the objective is to ensure that u can reach any point in B from any point in A. We propose spatial and temporal cloaking transformations to preserve user privacy, and we show experimentally that privacy can be achieved without significant quality of service deterioration. With advances in sensing and positioning technology, fueled by the ubiquitous deployment of wireless networks, location-aware computing has become a fundamental model for offering a wide range of life enhancing services. However, the ability to locate users and mobile objects opens doors for new threats - the intrusion of location privacy. Location anonymization refers to the process of perturbing the exact location of users as a cloaking region such that a user's location becomes indistinguishable from the location of a set of other users. A fundamental limitation of existing location anonymization techniques is that location information once perturbed to provide a certain anonymity level cannot be reversed to reduce anonymity or the degree of perturbation. This is especially a serious limiting factor in multi-level privacy-controlled scenarios where different users of the location information have different levels of access. This paper presents ReverseCloak, a new class of reversible location cloaking mechanisms that effectively support multi-level location privacy, allowing selective de-anonymization of the cloaking region to reduce the granularity of the perturbed location when suitable access credentials are provided. We evaluate the ReverseCloak techniques through extensive experiments on realistic road network traces generated by GTMobiSim. Our experiments show that the proposed techniques are efficient, scalable and provide the required level of privacy. The continuous advances in sensing and positioning technologies have resulted in a dramatic increase in popularity of Location-Based Services (LBS). Nevertheless, the LBS can lead to user privacy breach due to sharing location information with potentially malicious services. A high degree of location privacy preservation for LBS is extremely required. In this paper, a clustering K-anonymity scheme for location privacy preservation (namely CK) is proposed. The CK scheme does not rely on a trusted third party to anonymize the location information of users. In CK scheme, the whole area that all the users reside is divided into clusters recursively in order to get cloaked area. The exact location information of the user is replaced by the cloaked spatial temporal boundary (STB) including K users. The user can adjust the resolution of location information with spatial or temporal constraints to meet his personalized privacy requirement. The experimental results show that CK can provide stringent privacy guarantees, strong robustness and high QoS (Quality of Service).