What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy

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

Privacy-Preserving Data Collection and Sharing in Modern Mobile Internet Systems

With the ubiquity and widespread use of mobile devices such as laptops, smartphones, smartwatches, and IoT devices, large volumes of user data are generated and recorded. While there is great value in collecting, analyzing and sharing this data for improving products and services, data privacy poses a major concern. This dissertation research addresses the problem of privacy-preserving data collection and sharing in the context of both mobile trajectory data and mobile Internet access data. The first contribution of this dissertation research is the design and development of a system for utility-aware synthesis of differentially private and attack-resilient location traces, called AdaTrace. Given a set of real location traces, AdaTrace executes a four-phase process consisting of feature extraction, synopsis construction, noise injection, and generation of synthetic location traces. Compared to representative prior approaches, the location traces generated by AdaTrace offer up to 3-fold improvement in utility, measured using a variety of utility metrics and datasets, while preserving both differential privacy and attack resilience. The second contribution of this dissertation research is the design and development of locally private protocols for privacy-sensitive collection of mobile and Web user data. Motivated by the excessive utility loss of existing Local Differential Privacy (LDP) protocols under small user populations, this dissertation introduces the notion of Condensed Local Differential Privacy (CLDP) and a suite of protocols satisfying CLDP to enable the collection of various types of user data, ranging from ordinal data types in finite metric spaces (malware infection statistics), to non-ordinal items (OS versions and transaction categories), and to sequences of ordinal or non-ordinal items. Using cybersecurity data and case studies from Symantec, a major cybersecurity vendor, we show that proposed CLDP protocols are practical for key tasks including malware outbreak detection, OS vulnerability analysis, and inspecting suspicious activities on infected machines. The third contribution of this dissertation research is the development of a framework and a prototype system for evaluating privacy-utility tradeoffs of different LDP protocols, called LDPLens. LDPLens introduces metrics to evaluate protocol tradeoffs based on factors such as the utility metric, the data collection scenario, and the user-specified adversary metric. We develop a common Bayesian adversary model to analyze LDP protocols, and we formally and experimentally analyze Adversarial Success Rate (ASR) under each protocol. Motivated by the findings that numerous factors impact the ASR and utility behaviors of LDP protocols, we develop LDPLens to provide effective recommendations for finding the most suitable protocol in a given setting. Our three case studies with real-world datasets demonstrate that using the protocol recommended by LDPLens can offer substantial reduction in utility loss or in ASR, compared to using a randomly chosen protocol.Ph.D

Evaluating Privacy-Friendly Mobility Analytics on Aggregate Location Data

Information about people's movements and the locations they visit enables a wide number of mobility analytics applications, e.g., real-time traffic maps or urban planning, aiming to improve quality of life in modern smart-cities. Alas, the availability of users' fine-grained location data reveals sensitive information about them such as home and work places, lifestyles, political or religious inclinations. In an attempt to mitigate this, aggregation is often employed as a strategy that allows analytics and machine learning tasks while protecting the privacy of individual users' location traces. In this thesis, we perform an end-to-end evaluation of crowdsourced privacy-friendly location aggregation aiming to understand its usefulness for analytics as well as its privacy implications towards users who contribute their data. First, we present a time-series methodology which, along with privacy-friendly crowdsourcing of aggregate locations, supports mobility analytics such as traffic forecasting and mobility anomaly detection. Next, we design quantification frameworks and methodologies that let us reason about the privacy loss stemming from the collection or release of aggregate location information against knowledgeable adversaries that aim to infer users' profiles, locations, or membership. We then utilize these frameworks to evaluate defenses ranging from generalization and hiding, to differential privacy, which can be employed to prevent inferences on aggregate location statistics, in terms of privacy protection as well as utility loss towards analytics tasks. Our results highlight that, while location aggregation is useful for mobility analytics, it is a weak privacy protection mechanism in this setting and that additional defenses can only protect privacy if some statistical utility is sacrificed. Overall, the tools presented in this thesis can be used by providers who desire to assess the quality of privacy protection before data release and its results have several implications about current location data practices and applications