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Carmela González Troncoso

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Related publications (42)

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GenoShare: Supporting Privacy-Informed Decisions for Sharing Individual-Level Genetic Data

Jean-Pierre Hubaux, Carmela González Troncoso, Juan Ramón Troncoso-Pastoriza

One major obstacle to developing precision medicine to its full potential is the privacy concerns related to genomic-data sharing. Even though the academic community has proposed many solutions to protect genomic privacy, these so far have not been adopted ...

IOS PRESS2020

DATASHARENETWORK A Decentralized Privacy-Preserving Search Engine for Investigative Journalists

Laurent Girod, Carmela González Troncoso, Wouter Lueks

Investigative journalists collect large numbers of digital documents during their investigations. These documents can greatly benefit other journalists' work. However, many of these documents contain sensitive information. Hence, possessing such documents ...

USENIX ASSOC2020

Rethinking Location Privacy for Unknown Mobility Behaviors

Carmela González Troncoso

Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their designs and evaluate their privacy propert ...

IEEE COMPUTER SOC2019

GenoShare: Supporting Privacy-Informed Decisions for Sharing Exact Genomic Data

Jean-Pierre Hubaux, Jean Louis Raisaro, Carmela González Troncoso, Mathias Jacques Jean-Marc Humbert

The academic community has proposed many solutions to address the privacy concerns associated with genomic-data sharing. However, practitioners have not adopted these solutions due to their impact on the data utility. To address this problem, we introduce ...

EPFL infoscience2018

Deploying Decentralized, Privacy-Preserving Proximity Tracing

Jean-Pierre Hubaux, Mathias Josef Payer, James Richard Larus, Marcel Salathé, Edouard Bugnion, Carmela González Troncoso, Apostolos Pyrgelis, Wouter Lueks, Sylvain Chatel, Theresa Stadler

2022

Synthetic Data - Anonymisation Groundhog Day

Carmela González Troncoso, Theresa Stadler

Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the stat ...

USENIX ASSOC2022

Tandem: Securing Keys by Using a Central Server While Preserving Privacy

Carmela González Troncoso, Wouter Lueks

Users' devices, e.g., smartphones or laptops, are typically incapable of securely storing and processing cryptographic keys. We present Tandem, a novel set of protocols for securing cryptographic keys with support from a central server. Tandem uses one-tim ...

2020

VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption

Jean-Pierre Hubaux, Carmela González Troncoso, Christian Louis Knabenhans

Homomorphic encryption has become a practical solution for protecting the privacy of computations on sensitive data. However, existing homomorphic encryption pipelines do not guarantee the correctness of the computation result in the presence of a maliciou ...

Association for Computing Machinery, Inc2024

Helium: Scalable MPC among Lightweight Participants and under Churn

Carmela González Troncoso

We introduce Helium, a novel framework that supports scalable secure multiparty computation (MPC) for lightweight participants and tolerates churn. Helium relies on multiparty homomorphic encryption (MHE) as its core building block. While MHE schemes have ...

Association for Computing Machinery, Inc2024

Designing for data protection

Carmela González Troncoso

Humanitarian Organizations assist the most vulnerable populations in extremely challenging circumstances. For reasons of efficiency, accountability, and out of a desire to help as many people as possible, Humanitarian Organizations increasingly rely on dig ...

Cambridge University Press2024

POTs: Protective Optimization Technologies

Carmela González Troncoso, Bogdan Kulynych, Rebekah Jean Overdorf

Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a n ...

ASSOC COMPUTING MACHINERY2020

Unraveling an Old Cloak: k-anonymity for Location Privacy

Jean-Pierre Hubaux, Julien Freudiger, Carmela González Troncoso, Reza Shokri

There is a rich collection of literature that aims at protecting the privacy of users querying location-based services. One of the most popular location privacy techniques consists in cloaking users' locations such that k users appear as potential senders ...

ACM2010

Early evidence of effectiveness of digital contact tracing for SARS-CoV-2 in Switzerland

Mathias Josef Payer, James Richard Larus, Marcel Salathé, Edouard Bugnion, Carmela González Troncoso, Wouter Lueks, Theresa Stadler, Daniele Antonioli

In the wake of the pandemic of coronavirus disease 2019 (COVID-19), contact tracing has become a key element of strategies to control the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Given the rapid and intense spread of SARS-CoV ...

E M H SWISS MEDICAL PUBLISHERS LTD2020

VOTEAGAIN: A scalable coercion-resistant voting system

Carmela González Troncoso, Wouter Lueks

The strongest threat model for voting systems considers coercion resistance: protection against coercers that force voters to modify their votes, or to abstain. Existing remote voting systems either do not provide this property; require expensive operation ...

USENIX ASSOC2020

Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications

Marcel Salathé, Carmela González Troncoso, Wouter Lueks

2021

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

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

PELTA - Shielding Multiparty-FHE against Malicious Adversaries

Ali Utkan Sahin, Jean-Pierre Hubaux, Carmela González Troncoso, Sylvain Chatel, Christian Vincent Mouchet

Multiparty fully homomorphic encryption (MFHE) schemes enable multiple parties to efficiently compute functions on their sensitive data while retaining confidentiality. However, existing MFHE schemes guarantee data confidentiality and the correctness of th ...

Assoc Computing Machinery2023

Knock Knock, Who's There? Membership Inference on Aggregate Location Data

Carmela González Troncoso, Apostolos Pyrgelis

Aggregate location data is often used to support smart services and applications, e.g., generating live traffic maps or predicting visits to businesses. In this paper, we present the first study on the feasibility of membership inference attacks on aggrega ...

2018

A research agenda for digital proximity tracing apps

Marcel Salathé, Edouard Bugnion, Carmela González Troncoso, Theresa Stadler

E M H SWISS MEDICAL PUBLISHERS LTD2020

Why the search for a privacy-preserving data sharing mechanism is failing

Carmela González Troncoso, Theresa Stadler

The rapidly growing demand to share data more openly creates a need for secure and privacy-preserving sharing technologies. However, there are multiple challenges associated with the development of a universal privacy-preserving data sharing mechanism, and ...

SPRINGERNATURE2022