Zobrazeno 1 - 10
of 153
pro vyhledávání: '"TRONCOSO, CARMELA"'
Autor:
Luvison, Eva, Chatel, Sylvain, Sukaitis, Justinas, Narbel, Vincent Graf, Troncoso, Carmela, Lueks, Wouter
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
Externí odkaz:
http://arxiv.org/abs/2410.15942
Autor:
Kulynych, Bogdan, Gomez, Juan Felipe, Kaissis, Georgios, Calmon, Flavio du Pin, Troncoso, Carmela
Differential privacy (DP) is a widely used approach for mitigating privacy risks when training machine learning models on sensitive data. DP mechanisms add noise during training to limit the risk of information leakage. The scale of the added noise i
Externí odkaz:
http://arxiv.org/abs/2407.02191
Information-based attacks on social media, such as disinformation campaigns and propaganda, are emerging cybersecurity threats. The security community has focused on countering these threats on social media platforms like X and Reddit. However, they
Externí odkaz:
http://arxiv.org/abs/2406.08084
Privacy-enhancing blocking tools based on filter-list rules tend to break legitimate functionality. Filter-list maintainers could benefit from automated breakage detection tools that allow them to proactively fix problematic rules before deploying th
Externí odkaz:
http://arxiv.org/abs/2405.05196
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 tri
Externí odkaz:
http://arxiv.org/abs/2403.03792
Autor:
Raynal, Mathilde, Troncoso, Carmela
Collaborative Machine Learning (CML) allows participants to jointly train a machine learning model while keeping their training data private. In many scenarios where CML is seen as the solution to privacy issues, such as health-related applications,
Externí odkaz:
http://arxiv.org/abs/2402.13700
Autor:
Stadler, Theresa, Kulynych, Bogdan, Gastpar, Michael C., Papernot, Nicolas, Troncoso, Carmela
The promise of least-privilege learning -- to find feature representations that are useful for a learning task but prevent inference of any sensitive information unrelated to this task -- is highly appealing. However, so far this concept has only bee
Externí odkaz:
http://arxiv.org/abs/2402.12235
Autor:
EdalatNejad, Kasra, Lueks, Wouter, Sukaitis, Justinas, Narbel, Vincent Graf, Marelli, Massimo, Troncoso, Carmela
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 recip
Externí odkaz:
http://arxiv.org/abs/2308.02907
Research on adversarial robustness is primarily focused on image and text data. Yet, many scenarios in which lack of robustness can result in serious risks, such as fraud detection, medical diagnosis, or recommender systems often do not rely on image
Externí odkaz:
http://arxiv.org/abs/2306.04064
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,
Externí odkaz:
http://arxiv.org/abs/2303.17343