Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Arcolezi, Heber H."'
Machine learning (ML) algorithms rely primarily on the availability of training data, and, depending on the domain, these data may include sensitive information about the data providers, thus leading to significant privacy issues. Differential privac
Externí odkaz:
http://arxiv.org/abs/2405.14725
Autor:
Makhlouf, Karima, Arcolezi, Heber H., Zhioua, Sami, Brahim, Ghassen Ben, Palamidessi, Catuscia
Automated decision systems are increasingly used to make consequential decisions in people's lives. Due to the sensitivity of the manipulated data as well as the resulting decisions, several ethical concerns need to be addressed for the appropriate u
Externí odkaz:
http://arxiv.org/abs/2312.04404
Autor:
Binkytė, Rūta, Pinzón, Carlos, Lestyán, Szilvia, Jung, Kangsoo, Arcolezi, Héber H., Palamidessi, Catuscia
Differential privacy is a widely adopted framework designed to safeguard the sensitive information of data providers within a data set. It is based on the application of controlled noise at the interface between the server that stores and processes t
Externí odkaz:
http://arxiv.org/abs/2311.04037
Autor:
Arcolezi, Héber H., Gambs, Sébastien
Publikováno v:
Arcolezi, H\'eber H., and S\'ebastien Gambs. "Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective." Proceedings on Privacy Enhancing Technologies 4 (2024): 123-141
While the existing literature on Differential Privacy (DP) auditing predominantly focuses on the centralized model (e.g., in auditing the DP-SGD algorithm), we advocate for extending this approach to audit Local DP (LDP). To achieve this, we introduc
Externí odkaz:
http://arxiv.org/abs/2309.01597
This paper investigates the utility gain of using Iterative Bayesian Update (IBU) for private discrete distribution estimation using data obfuscated with Locally Differentially Private (LDP) mechanisms. We compare the performance of IBU to Matrix Inv
Externí odkaz:
http://arxiv.org/abs/2307.07744
In recent years, Local Differential Privacy (LDP), a robust privacy-preserving methodology, has gained widespread adoption in real-world applications. With LDP, users can perturb their data on their devices before sending it out for analysis. However
Externí odkaz:
http://arxiv.org/abs/2304.12845
Collecting and analyzing evolving longitudinal data has become a common practice. One possible approach to protect the users' privacy in this context is to use local differential privacy (LDP) protocols, which ensure the privacy protection of all use
Externí odkaz:
http://arxiv.org/abs/2210.00262
The private collection of multiple statistics from a population is a fundamental statistical problem. One possible approach to realize this is to rely on the local model of differential privacy (LDP). Numerous LDP protocols have been developed for th
Externí odkaz:
http://arxiv.org/abs/2209.01684
Autor:
Arcolezi, Héber H., Couchot, Jean-François, Gambs, Sébastien, Palamidessi, Catuscia, Zolfaghari, Majid
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-world implementations by big tech compa
Externí odkaz:
http://arxiv.org/abs/2205.02648
This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-of-the-art formal notion, has been used as the privacy guarantee in two
Externí odkaz:
http://arxiv.org/abs/2205.00436