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Article
Improving fairness generalization through a sample-robust optimization method
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 mitiga...
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Chapter and Conference Paper
Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python
This paper introduces the multi-freq-ldpy Python package for multiple frequency estimation under Local Differential Privacy (LDP) guarantees. LDP is a gold standard for achieving local privacy with several real-w...
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Chapter and Conference Paper
Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists
Fairness and interpretability are fundamental requirements for the development of responsible machine learning. However, learning optimal interpretable models under fairness constraints has been identified as ...
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Chapter and Conference Paper
Publication of Court Records: Circumventing the Privacy-Transparency Trade-Off
The open data movement is leading to the massive publishing of court records online, increasing the transparency and accessibility of justice, and enabling the advent of legal technologies building on the weal...
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Chapter and Conference Paper
Privacy and Ethical Challenges in Big Data
The advent of Big Data coupled with the profiling of users has lead to the development of services and decision-making processes that are highly personalized, but also raise fundamental privacy and ethical iss...
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Article
Open AccessOn the privacy-conscientious use of mobile phone data
The breadcrumbs we leave behind when using our mobile phones—who somebody calls, for how long, and from where—contain unprecedented insights about us and our societies. Researchers have compared the recent ava...
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Article
Optimal noise functions for location privacy on continuous regions
Users of location-based services are highly vulnerable to privacy risks since they need to disclose, at least partially, their locations to benefit from these services. One possibility to limit these risks is ...
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Chapter and Conference Paper
Private eCash in Practice (Short Paper)
Most electronic payment systems for applications, such as eTicketing and eToll, involve a single entity acting as both merchant and bank. In this paper, we propose an efficient privacy-preserving post-payment ...
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Chapter and Conference Paper
The Not-so-Distant Future: Distance-Bounding Protocols on Smartphones
In authentication protocols, a relay attack allows an adversary to impersonate a legitimate prover, possibly located far away from a verifier, by simply forwarding messages between these two entities. The effe...
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Chapter and Conference Paper
Sanitization of Call Detail Records via Differentially-Private Bloom Filters
Publishing directly human mobility data raises serious privacy issues due to its inference potential, such as the (re-)identification of individuals. To address these issues and to foster the development of su...
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Chapter and Conference Paper
The Crypto-Democracy and the Trustworthy (Position Paper)
In the current architecture of the Internet, there is a strong asymmetry in terms of power between the entities that gather and process personal data (e.g., major Internet companies, telecom operators, cloud prov...
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Chapter and Conference Paper
Private Asymmetric Fingerprinting: A Protocol with Optimal Traitor Tracing Using Tardos Codes
Active fingerprinting schemes were originally invented to deter malicious users from illegally releasing an item, such as a movie or an image. To achieve this, each time an item is released, a different finger...
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Chapter and Conference Paper
A Privacy-Preserving Contactless Transport Service for NFC Smartphones
The development of NFC-enabled smartphones has paved the way to new applications such as mobile payment (m-payment) and mobile ticketing (m-ticketing). However, often the privacy of users of such services is e...
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Chapter and Conference Paper
Challenging Differential Privacy:The Case of Non-interactive Mechanisms
In this paper, we consider personalized recommendation systems in which before publication, the profile of a user is sanitized by a non-interactive mechanism compliant with the concept of differential privacy....
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Article
Open AccessQuantum speed-up for unsupervised learning
We show how the quantum paradigm can be used to speed up unsupervised learning algorithms. More precisely, we explain how it is possible to accelerate learning algorithms by quantizing some of their subroutine...
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Chapter and Conference Paper
SlopPy: Slope One with Privacy
In order to contribute to solve the personalization/privacy paradox, we propose a privacy-preserving architecture for one of state-of-the-art recommendation algorithm, Slope One. More precisely, we describe Sl...
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Chapter
On the Power of the Adversary to Solve the Node Sampling Problem
We study the problem of achieving uniform and fresh peer sampling in large scale dynamic systems under adversarial behaviors. Briefly, uniform and fresh peer sampling guarantees that any node in the system is ...
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Chapter and Conference Paper
Reconstruction Attack through Classifier Analysis
In this paper, we introduce a novel inference attack that we coin as the reconstruction attack whose objective is to reconstruct a probabilistic version of the original dataset on which a classifier was learnt...
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Chapter
Maintaining Sovereignty over Personal Data in Social Networking Sites
The rise of social networking sites (SNS) such as Facebook, MySpace, and LinkedIn has provided a platform for individuals to easily stay in touch with friends, family, and colleagues and actively encourage the...
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Chapter and Conference Paper
BLIP: Non-interactive Differentially-Private Similarity Computation on Bloom filters
In this paper, we consider the scenario in which the profile of a user is represented in a compact way, as a Bloom filter, and the main objective is to privately compute in a distributed manner the similarity ...
