As communications-enabled devices are becoming more ubiquitous, it becomes easier to track the movements of individuals through the radio signals broadcasted by their devices. Thus, while there is a strong interest for physical analytics platforms to leverage this information for many purposes, this tracking also threatens the privacy of individuals. To solve this issue, we propose a privacy-preserving solution for collecting aggregate mobility patterns while satisfying the strong guarantee of ε-differential privacy. More precisely, we introduce a sanitization mechanism for efficient, privacy-preserving and non-interactive approximate distinct counting for physical analytics based on perturbed Bloom filters called Pan-Private BLIP. We also extend and generalize previous approaches for estimating distinct count of events and joint events (i.e., intersection and more generally t-out-of-n cardinalities). Finally, we evaluate expirementally our approach and compare it to previous ones on real datasets.

With the widespread development of the quantified-self movement, an increasing number of users rely on mobile applications to monitor their physical activity through their smartphones. However, granting applications a direct access to sensor data exposes users to privacy risks. In particular, motion sensor data are usually transmitted to analytics applications hosted in the cloud, which leverages on machine learning models to provide feedback on their activity status to users. In this setting, nothing prevents the service provider to infer private and sensitive information about a user such as health or demographic attributes. To address this issue, we propose DySan, a privacy-preserving framework to sanitize motion sensor data against unwanted sensitive inferences (i.e., improving privacy) while limiting the loss of accuracy on the physical activity monitoring (i.e., maintaining data utility). Our approach is inspired from the framework of Generative Adversarial Networks to sanitize the sensor data for the purpose of ensuring a good trade-off between utility and privacy. More precisely, by learning in a competitive manner several networks, DySan is able to build models that sanitize motion data against inferences on a specified sensitive attribute (e.g., gender) while maintaining an accurate activity recognition. DySan builds various sanitizing models, characterized by different sets of hyperparameters in the global loss function, to propose a transfer learning scheme over time by dynamically selecting the model which provides the best utility and privacy trade-off according to the incoming data. Experiments conducted on real datasets demonstrate that DySan can drastically limit the gender inference up to 41% (from 98% with raw data to 57% with sanitized data) while only reducing the accuracy of activity recognition by 3% (from 95% with raw data to 92% with sanitized data).

Unwanted bias is a major concern in machine learning, raising in particular significant ethical issues when machine learning models are deployed within high-stakes decision systems. A common solution to mitigate it is to integrate and optimize a statistical fairness metric along with accuracy during the training phase. However, one of the main remaining challenges is that current approaches usually generalize poorly in terms of fairness on unseen data. We address this issue by proposing a new robustness framework for statistical fairness in machine learning. The proposed approach is inspired by the domain of distributionally robust optimization and works in ensuring fairness over a variety of samplings of the training set. Our approach can be used to quantify the robustness of fairness but also to improve it when training a model. We empirically evaluate the proposed method and show that it effectively improves fairness generalization. In addition, we propose a simple yet powerful heuristic application of our framework that can be integrated into a wide range of existing fair classification techniques to enhance fairness generalization. Our extensive empirical study using two existing fair classification methods demonstrates the efficiency and scalability of the proposed heuristic approach.

The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial training approach called GANSan for learning a sanitizer whose objective is to prevent the possibility of any discrimination (i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our method GANSan is partially inspired by the powerful framework of generative adversarial networks (in particular Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible, thus preserving the interpretability of the sanitized data. Consequently, once the sanitizer is trained, it can be applied to new data locally by an individual on their profile before releasing it. Finally, experiments on real datasets demonstrate the effectiveness of the approach as well as the achievable trade-off between fairness and utility.

We consider the problem of securely conducting a poll in synchronous dynamic networks equipped with a Public Key Infrastructure (PKI). Whereas previous distributed solutions had a communication cost of O(n ² ) in an n nodes system, we present SPP (Secure and Private Polling), the first distributed polling protocol requiring only a communication complexity of O(n log ³ n), which we prove is near-optimal. Our protocol ensures perfect security against a computationally-bounded adversary, tolerates (1/2 - ϵ)n Byzantine nodes for any constant 1/2 >; ϵ >; 0 (not depending on n), and outputs the exact value of the poll with high probability. SPP is composed of two sub-protocols, which we believe to be interesting on their own: SPP-Overlay maintains a structured overlay when nodes leave or join the network, and SPP-Computation conducts the actual poll. We validate the practicality of our approach through experimental evaluations and describe briefly two possible applications of SPP: (1) an optimal Byzantine Agreement protocol whose communication complexity is Θ(n log n) and (2) a protocol solving an open question of King and Saia in the context of aggregation functions, namely on the feasibility of performing multiparty secure aggregations with a communication complexity of o(n ² ).