26 citations · 54 across the 7 of their papers we have counts for
7 papers
Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy
Daniil Filienko, Yinzhou Wang, Caroline El Jazmi +4
While Large Language Models (LLMs) are being quickly adapted to many domains, including healthcare, their strengths and pitfalls remain under-explored. In our study, we examine the…
High Epsilon Synthetic Data Vulnerabilities in MST and PrivBayes
Steven Golob, Sikha Pentyala, Anuar Maratkhan +1
Synthetic data generation (SDG) has become increasingly popular as a privacy-enhancing technology. It aims to maintain important statistical properties of its underlying training d…
Privacy-Preserving Fair Item Ranking
Jia Ao Sun, Sikha Pentyala, Martine De Cock +1
Users worldwide access massive amounts of curated data in the form of rankings on a daily basis. The societal impact of this ease of access has been studied and work has been done…
Privacy-Preserving Feature Selection with Secure Multiparty Computation
Xiling Li, Rafael Dowsley, Martine De Cock
Existing work on privacy-preserving machine learning with Secure Multiparty Computation (MPC) is almost exclusively focused on model training and on inference with trained models,…
Privacy-Preserving Video Classification with Convolutional Neural Networks
Sikha Pentyala, Rafael Dowsley, Martine De Cock
Many video classification applications require access to personal data, thereby posing an invasive security risk to the users' privacy. We propose a privacy-preserving implementati…
Possibilistic Answer Set Programming Revisited
Kim Bauters, Steven Schockaert, Martine De Cock +1
Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an appli…