Publications associées à Dissent in Numbers: Making Strong Anonymity Scale

Publications associées (29)

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

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

Privatized graph federated learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

SPRINGER2023

Non-verbal Communication between Humans and Robots: Imitation, Mutual Understanding and Inferring Object Properties

Nuno Ricardo Ferreira Duarte

Humans can express their actions and intentions, resorting to verbal and/or non-verbal communication. In verbal communication, humans use language to express, in structured linguistic terms, the desired action they wish to perform. Non-verbal communication ...

EPFL2023

Moby: A Blackout-Resistant Anonymity Network for Mobile Devices

Bryan Alexander Ford, Antoine Rault

Internet blackouts are challenging environments for anonymity and censorship resistance. Existing popular anonymity networks (e.g., Freenet, I2P, Tor) rely on Internet connectivity to function, making them impracticable during such blackouts. In such a set ...

2022

Genuinely distributed Byzantine machine learning

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault, Le Nguyen Hoang, Arsany Hany Abdelmessih Guirguis

Machine learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

2022

Analyzing and Protecting Communication Metadata

Ludovic Barman

Most communication systems (e.g., e-mails, instant messengers, VPNs) use encryption to prevent third parties from learning sensitive information. However, encrypted communications protect the contents but often leak metadata: the amount of data sent and th ...

EPFL2021

Integrity and Metadata Protection in Data Retrieval

Kirill Nikitin

Secure retrieval of data requires integrity, confidentially, transparency, and metadata-privacy of the process. Existing protection mechanisms, however, provide only partially these properties: encryption schemes still expose cleartext metadata, protocols ...

EPFL2021

When to Hedge in Interactive Services

Aikaterini Argyraki, Edouard Bugnion, Mia Primorac

In online data-intensive (OLDI) services, each client request typically executes on multiple servers in parallel; as a result, “system hiccups”, although rare within a single server, can interfere with many client requests and cause violations of service-l ...

USENIX ASSOC2021

GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

Rachid Guerraoui, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

We present GARFIELD, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine–resilient. GARFIELD relies on a novel object–oriented design, reducing t ...

IEEE2021

Encrypted DNS double right arrow Privacy? A Traffic Analysis Perspective

Carmela González Troncoso, Sandra Deepthy Siby

Virtually every connection to an Internet service is preceded by a DNS lookup. Lookups are performed without any traffic-level protection, thus enabling manipulation, redirection, surveillance, and censorship. To address these issues, large organizations s ...

INTERNET SOC2020

Cooperative Data Exchange and Private Information Retrieval

Su Li

Coding techniques have been well studied and used for improving communication quality by combating noise and mitigating interference. Recently, it has been shown that the same coding techniques can also be exploited to further improve communication perform ...

EPFL2020

Genuinely Distributed Byzantine Machine Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault, Le Nguyen Hoang, Arsany Hany Abdelmessih Guirguis

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

Association for Computing Machinery2020

MorphIT: Morphing Packet Reports for Internet Transparency

Bryan Alexander Ford, Aikaterini Argyraki, Georgia Fragkouli

Can we improve Internet transparency without worsening user anonymity? For a long time, researchers have been proposing transparency systems, where traffic reports produced at strategic network points help assess network behavior and verify service-level a ...

Sciendo2019

Drowsy-DC: Data center power management system

Willy Zwaenepoel, Baptiste Joseph Eustache Lepers

In a modern data center (DC), a large majority of costs arise from energy consumption. The most popular technique used to mitigate this issue is virtualization and more precisely virtual machine (VM) consolidation. Although consolidation may increase serve ...

IEEE2019

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Kosar Haghshenas

Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource m ...

2022

Formal Approaches to Querying Big Data in Shared-Nothing Systems

Bas Ketsman

To meet today's data management needs, it is a widespread practice to use distributed data storage and processing systems. Since the publication of the MapReduce paradigm, a plethora of such systems arose, but although widespread, the capabilities of these ...

ASSOC COMPUTING MACHINERY2019

Systems and method for anonymous, low-latencey, tracking-resistant communications in a networked environment

Jean-Pierre Hubaux, Bryan Alexander Ford, Ludovic Barman, Italo Ivan Dacosta Petrocelli

Exemplary embodiments of the present disclosure provide cryptographically-strong tracking protection with unnoticeable "single-hop" network latencies by leveraging a client-relay- server architecture, in which the clients, relays (trusted or untrusted), an ...

2018

Causal Consistency and Latency Optimality: Friend or Foe? [Extended Version]

Rachid Guerraoui, Willy Zwaenepoel, Diego Didona, Jingjing Wang

Causal consistency is an attractive consistency model for geo-replicated data stores. It is provably the strongest model that tolerates network partitions. It avoids the long latencies associated with strong consistency, and, especially when using read-onl ...

2018

Causal Consistency and Latency Optimality: Friend or Foe?

Rachid Guerraoui, Willy Zwaenepoel, Diego Didona, Jingjing Wang

Causal consistency is an attractive consistency model for geo-replicated data stores. It is provably the strongest model that tolerates network partitions. It avoids the long latencies associated with strong consistency, and, especially when using read-onl ...

ASSOC COMPUTING MACHINERY2018

Caching and Distributed Storage : Models, Limits and Designs

Saeid Sahraei

A simple task of storing a database or transferring it to a different point via a communication channel turns far more complex as the size of the database grows large. Limited bandwidth available for transmission plays a central role in this predicament. I ...

EPFL2018