2011 IEEE Symposium on Security and Privacy Quantifying Location Privacy

Citations in the corpus

• Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, background): “Sharing of location information is often associated with serious privacy threats [3], [35], [36].”
• EnCore: private, context-based communication for mobile social apps (2014, background): “Location privacy Several works investigate location privacy for mobile devices [34, 38, 53, 32, 35].”
• What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, background): “d the privacy loss incurred when disclosing obfuscated traces of individual users, e.g., when using location-based services. The main work in this area is the quantification framework by Shokri et al. [41,42], which considers a strategic adversary that has prior information about users’ mobility patt”
• Pseudonym Schemes in Vehicular Networks: A Survey (2015, background): “size of the anonymity set [53]–[55], the entropy [44], [56]–[58], and the adversary’s tracking uncertainty [46], [59]–[61] (see”
• PrivCheck: privacy-preserving check-in data publishing for personalized location based services (2016, background): “Most of the research efforts focus on location privacy [38, 21, 37], i.e., protecting user location information before using them.”
• Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms (2015, background): “Because it is difficult for users to estimate the privacy risks that stem from location sharing (it usually requires to perform statistical inference [30]) and because it would be cumbersome for users to manually select the level of obfuscation to apply to each of their check-ins, automatic obfuscat”
• Protecting Locations with Differential Privacy under Temporal Correlations (2014, methodology): “While such temporal correlations can be commonly modeled by Markov chain [37, 17, 25], and few works have considered such Markov models [37, 17], it remains a challenge to provide rigorous privacy protection under temporal correlations for continual location sharing.”
• Geo-indistinguishability: differential privacy for location-based systems (2012, background): “Expectation of distance error [35, 36, 23] is a natural way to quantify the privacy offered by a location-obfuscation mechanism.”
• Technical Privacy Metrics (2015, methodology): “t the absolute value of entropy does not convey much meaning [62]. Entropy gives an indication of the adversary’s uncertainty, but does not state how correct or accurate the adversary’s estimates are [130]. For example, the adversary could be certain but wrong (low correctness) if the estimate indic”
• MLCapsule: Guarded Offline Deployment of Machine Learning as a Service (2018, unknown): “, |Pδ − P ′ δ|, this is also referred to as the expected estimation error [30, 5, 40].”
• Evaluating User Privacy in Bitcoin (2013, unknown): “Finally, in [18], Shokri et al.”
• Protecting location privacy: optimal strategy against localization attacks (2012, methodology): “In a follow-up of this work, Shokri et al. provide a framework [25, 26] to quantify location privacy.”
• Deanonymizing mobility traces: using social network as a side-channel (2012, background): “[30] proposed a solution to quantify location privacy with the goal of providing a unified framework that can be used to evaluate various location obfuscation mechanisms.”
• Privacy and accountability for location-based aggregate statistics (2011, background): “[38] offer a framework for quantifying privacy leakage in various location privacy schemes that also takes into account side information.”
• Broadening the Scope of Differential Privacy Using Metrics (2013, background): “Some of these works [19,20,21] propose the use of the expectation of distance error of the attacker as the way to quantify the privacy offered by a mechanism.”
• Measuring Large-Scale Social Networks with High Resolution (2014, methodology): “A significant amount of literature exists regarding the possible attacks that can be performed on personal data, such as unauthorized analysis [71] with a view to decoding daily routines [72] or friendships [42] of Measuring Large-Scale Social Networks with High Resolution”
• Privacy Risks of General-Purpose Language Models (2020, background): “On location privacy, for example, Shokri et al. [64] used Markov chain modeling to reconstruct the actual traces of users from obfuscated location information, while some recent works exploit side channels from social media like hashtags for location inference using clustering [8] or random forests…”
• Achieving k-anonymity in privacy-aware location-based services (2014, background): “Some other metrics [24], [25] are based on the estimation error of the adversary to quantify the location privacy.”
• Structural Data De-anonymization: Quantification, Practice, and Implications (2014, background): “However, on the other hand, the publicly availability of users’ mobility trace data causes a potentially serious threat to users’ privacy and even themselves [3][19][20].”
• A Survey of Physics-Based Attack Detection in Cyber-Physical Systems (2018, background): “There is also ongoing research on CPS privacy including smart grids [43], vehicular location monitoring [37], and location privacy [91].”