Publications by Carmela González Troncoso

Reimagining Wearable-Based Digital Contact Tracing: Insights from Kenya and Côte d’Ivoire

While digital contact tracing has been extensively studied in Western contexts, its relevance and application in Africa remain largely unexplored. This study focuses on Kenya and Côte d’Ivoire to uncover user perceptions and inform the design of culturally ...

ACM2025

A Low-Cost Privacy-Preserving Digital Wallet for Humanitarian Aid Distribution

Carmela González Troncoso

Humanitarian organizations distribute aid to people affected by armed conflicts or natural disasters. Digitalization has the potential to increase the efficiency and fairness of aid-distribution systems, and recent work by Wang et al. has shown that these ...

IEEE2025

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

Bugs in our pockets: the risks of client-side scanning

Carmela González Troncoso

Our increasing reliance on digital technology for personal, economic, and government affairs has made it essential to secure the communications and devices of private citizens, businesses, and governments. This has led to pervasive use of cryptography acro ...

Oxford Univ Press2024

Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data

Carmela González Troncoso, Dario Pasquini

We introduce the concept of "universal"password model - a password model that, once pre-trained, can automatically adapt its guessing strategy based on the target system. To achieve this, the model does not need to access any plaintext passwords from the t ...

Institute of Electrical and Electronics Engineers Inc.2024

VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption

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

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

Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks

Carmela González Troncoso

We introduce a new family of prompt injection attacks, termed Neural Exec. Unlike known attacks that rely on handcrafted strings (e.g., Ignore previous instructions and...), we show that it is possible to conceptualize the creation of execution triggers as ...

Association for Computing Machinery2024

Janus: Safe Biometric Deduplication for Humanitarian Aid Distribution

Kasra Edalatnejadkhamene, Carmela González Troncoso

Humanitarian organizations provide aid to people in need. To use their limited budget efficiently, their distribution processes must ensure that legitimate recipients cannot receive more aid than they are entitled to. Thus, it is essential that recipients ...

Institute of Electrical and Electronics Engineers Inc.2024

PELTA - Shielding Multiparty-FHE against Malicious Adversaries

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

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

Poster: Verifiable Encodings for Maliciously-Secure Homomorphic Encryption Evaluation

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

Homomorphic encryption has become a promising 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 ...

Assoc Computing Machinery2023

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

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

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

CookieGraph: Understanding and Detecting First-Party Tracking Cookies

Sandra Deepthy Siby, Carmela González Troncoso

As third-party cookie blocking is becoming the norm in mainstream web browsers, advertisers and trackers have started to use first-party cookies for tracking. To understand this phenomenon, we conduct a differential measurement study with versus without th ...

Assoc Computing Machinery2023

Bayes Security: A Not So Average Metric

Carmela González Troncoso, Giovanni Cherubin

Security system designers favor worst-case security metrics, such as those derived from differential privacy (DP), due to the strong guarantees they provide. On the downside, these guarantees result in a high penalty on the system's performance. In this pa ...

IEEE2023

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

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

WEBGRAPH: Capturing Advertising and Tracking Information Flows for Robust Blocking

Sandra Deepthy Siby, Carmela González Troncoso

Users rely on ad and tracker blocking tools to protect their privacy. Unfortunately, existing ad and tracker blocking tools are susceptible to mutable advertising and tracking content. In this paper, we first demonstrate that a state-of-the-art ad and trac ...

USENIX ASSOC2022

Deploying Decentralized, Privacy-Preserving Proximity Tracing

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

2022