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PriBots: Conversational Privacy with Chatbots

Publications associées (31)

The Privacy Power of Correlated Noise in Decentralized Learning

Rachid Guerraoui, Martin Jaggi, Anastasiia Koloskova, Youssef Allouah, Aymane El Firdoussi

Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, wi ...

PMLR2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Differentially private multi-agent constraint optimization

Boi Faltings, Aleksei Triastcyn, Sujit Prakash Gujar, Sankarshan Damle

Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...

Springer2024

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Sayan Biswas

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries du ...

Ieee-Inst Electrical Electronics Engineers Inc2024

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

Touradj Ebrahimi, Lin Yuan, Xiao Pu, Hongbo Li, Yao Zhang

The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...

Ieee-Inst Electrical Electronics Engineers Inc2024

The Current State of the OBI DICT Project: A Bilingual e-Dictionary of Oracle-Bone Inscriptions with AI Image Recognition

This article reports on the current state of the OBI DICT project, a bilingual e-dictionary of oracle-bone inscriptions (OBI), incorporating artificial intelligence (AI) image recognition technology. It first provides a brief overview of the development of ...

Buro Van Die Wat2024

Secure Interface Design Leveraging Hardware/Software Support

Atri Bhattacharyya

Computer systems rely heavily on abstraction to manage the exponential growth of complexity across hardware and software. Due to practical considerations of compatibility between components of these complex systems across generations, developers have favou ...

EPFL2024

When Your AI Becomes a Target: AI Security Incidents and Best Practices

Alexandre Massoud Alahi, Kathrin Grosse

In contrast to vast academic efforts to study AI security, few real-world reports of AI security incidents exist. Released incidents prevent a thorough investigation of the attackers' motives, as crucial information about the company and AI application is ...

AAAI Press2024

Stochastic pairwise preference convergence in Bayesian agents

Max-Olivier Hongler

Beliefs inform the behaviour of forward-thinking agents in complex environments. Recently, sequential Bayesian inference has emerged as a mechanism to study belief formation among agents adapting to dynamical conditions. However, we lack critical theory to ...

2024

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Bogdan Kulynych

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

EPFL2023

Bridging the gap between theoretical and practical privacy technologies for at-risk populations

Kasra Edalatnejadkhamene

With the pervasive digitalization of modern life, we benefit from efficient access to information and services. Yet, this digitalization poses severe privacy challenges, especially for special-needs individuals. Beyond being a fundamental human right, priv ...

EPFL2023

Privacy-Preserving Machine Learning on Graphs

Sina Sajadmanesh

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

EPFL2023

P3LI5: Practical and confidEntial Lawful Interception on the 5G core

Apostolos Pyrgelis, Francesco Intoci

Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain insightful data from network communications for criminal proceedings, e.g., ne ...

IEEE2023

Multi-agent Learning with Privacy Guarantees

Elsa Rizk

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

EPFL2023

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Michael Christoph Gastpar, Aditya Pradeep

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

2023

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Bogdan Kulynych, Carmela González Troncoso

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

Assoc Computing Machinery2023

Privacy-preserving federated neural network training and inference

Sinem Sav

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

EPFL2023

Adapting Haptic Feedback for Guided Meditation

Nicolas Henchoz, Cédric Duchene, Andreas Sonderegger, Romain Simon Collaud, Emily Clare Groves, Yoann Pierre Douillet

Technology supporting meditation is a multimillion-dollar market that continues to grow. There is also strong academic interest to understand and improve the impact technology can have for the user experience of practitioners. However, little work investig ...

2023

Prompting Large Language Models to Power Educational Chatbots

Denis Gillet, Sandy Ingram, Juan Carlos Farah, Fanny Kim-Lan Lasne

The recent rise in both popularity and performance of large language models has garnered considerable interest regarding their applicability to education. Technologies like ChatGPT, which can engage in human-like dialog, have already disrupted educational ...

Springer2023

On the (In)security of Peer-to-Peer Decentralized Machine Learning

Mathilde Aliénor Raynal, Carmela González Troncoso, Dario Pasquini

In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning-a collaborative machine learning framework aimed at addressing the main limitations of federated learning. We introduce a suite of novel attacks for both passive and ...

IEEE COMPUTER SOC2023