Publication

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

Publications associées (29)

Enforcing Privacy in Distributed Learning With Performance Guarantees

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...

Ieee-Inst Electrical Electronics Engineers Inc2023

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

Multiparty Homomorphic Encryption: from Theory to Practice

Christian Vincent Mouchet

Multiparty homomorphic encryption (MHE) enables a group of parties to encrypt data in a way that (i) enables the evaluation of functions directly over its ciphertexts and (ii) enforces a joint cryptographic access-control over the underlying data. By exten ...

EPFL2023

Not Yet Another Digital ID: Privacy-Preserving Humanitarian Aid Distribution

Justinas Sukaitis, Carmela González Troncoso, Wouter Lueks, Boya Wang

Humanitarian aid-distribution programs help bring physical goods to people in need. Traditional paper-based solutions to support aid distribution do not scale to large populations and are hard to secure. Existing digital solutions solve these issues, at th ...

IEEE COMPUTER SOC2023

Fast and Durable Lithium Storage Enabled by Tuning Entropy in Wadsley-Roth Phase Titanium Niobium Oxides

Yuehui Li, Rui Xia, Kangning Zhao, Liping Zhong

Wadsley-Roth phase titanium niobium oxides have received considerable interest as anodes for lithium ion batteries. However, the volume expansion and sluggish ion/electron transport kinetics retard its application in grid scale. Here, fast and durable lith ...

WILEY-V C H VERLAG GMBH2023

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

Toward contactless human thermal monitoring: A framework for Machine Learning-based human thermo-physiology modeling augmented with computer vision

Alexandre Massoud Alahi, Dolaana Khovalyg, Mohamad Rida, Mohamed Ossama Ahmed Abdelfattah

The transition towards a human-centered indoor climate is beneficial from occupants’ thermal comfort and from an energy reduction perspective. However, achieving this goal requires the knowledge of the thermal state of individuals at the level of body part ...

2023

ROSMOSE: A Web-based Optimization Tool to Aid Decision-making for the Design and Operation of Industrial and Urban Energy Systems

François Maréchal, Cédric Terrier, Daniel Alexander Florez Orrego, Michel Lopez, Meire Ellen Gorete Ribeiro Domingos

Energy efficiency is crucial for the sustainable operation of all industrial and urban sectors. However, practicing engineers have seldom access to open-source tools that can readily evaluate and compare scenarios in terms of energy consumption, cost, and ...

2023

Practical Implementations of Remote Power Side-Channel and Fault-Injection Attacks on Multitenant FPGAs

Francesco Regazzoni, Mirjana Stojilovic, Ognjen Glamocanin, Dina Gamaleldin Ahmed Shawky Mahmoud

Cloud computing environments increasingly provision FPGAs because of their fine-grained, highly parallel, and flexible hardware architecture. The availability of FPGAs in the cloud fueled research on the security risks associated with exposing FPGA fabric ...

Springer, Cham2023