20 citations · 52 across the 8 of their papers we have counts for
8 papers
Privacy Vulnerabilities in Marginals-based Synthetic Data
Steven Golob, Sikha Pentyala, Anuar Maratkhan +1
When acting as a privacy-enhancing technology, synthetic data generation (SDG) aims to maintain a resemblance to the real data while excluding personally-identifiable information.…
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…
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection
Devin Reich, Ariel Todoki, Rafael Dowsley +2
Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal tex…
VirtualIdentity: Privacy-Preserving User Profiling
Sisi Wang, Wing-Sea Poon, Golnoosh Farnadi +6
User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies. Existing systems require that the UGC is ful…
Solving stable matching problems using answer set programming
Sofie De Clercq, Steven Schockaert, Martine De Cock +1
Since the introduction of the stable marriage problem (SMP) by Gale and Shapley (1962), several variants and extensions have been investigated. While this variety is useful to wide…
Characterizing and Extending Answer Set Semantics using Possibility Theory
Kim Bauters, Steven Schockaert, Martine De Cock +1
Answer Set Programming (ASP) is a popular framework for modeling combinatorial problems. However, ASP cannot easily be used for reasoning about uncertain information. Possibilistic…
Reducing Fuzzy Answer Set Programming to Model Finding in Fuzzy Logics
Jeroen Janssen, Steven Schockaert, Dirk Vermeir +1
In recent years answer set programming has been extended to deal with multi-valued predicates. The resulting formalisms allows for the modeling of continuous problems as elegantly…