13 citations · 33 across the 16 of their papers we have counts for
18 papers
Membership Inference Attack on Private Evolution
Daniil Filienko, Sikha Pentyala, Martine De Cock
Synthetic Data Generation (SDG) is an actively studied way to enable safe sharing of private data more broadly. Inference-only SDG methods, such as Private Evolution (PE), are part…
Federated Generation of Synthetic RNA-seq Data
Daniil Filienko, Martine De Cock, Sikha Pentyala
Access to genomic data is highly regulated due to its sensitive nature. While safeguards are essential, cumbersome data access processes pose a significant barrier to the developme…
FHAIM: Fully Homomorphic AIM For Private Synthetic Data Generation
Mayank Kumar, Qian Lou, Paulo Barreto +2
Data is the lifeblood of AI, yet much of the most valuable data remains locked in silos due to privacy and regulations. As a result, AI remains heavily underutilized in many of the…
Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
Liying Wang, Ph. D., Daffodil Carrington +8
Family caregivers often face substantial mental health challenges due to their multifaceted roles and limited resources. This study explored the potential of a large language model…
Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication
Daniil Filienko, Mahek Nizar, Javier Roberti +5
Tuberculosis (TB) is the leading cause of death from an infectious disease globally, with the highest burden in low- and middle-income countries. In these regions, limited healthca…
Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy
Khaoula Chehbouni, Martine De Cock, Gilles Caporossi +3
The increased screen time and isolation caused by the COVID-19 pandemic have led to a significant surge in cases of online grooming, which is the use of strategies by predators to…
End to End Collaborative Synthetic Data Generation
Sikha Pentyala, Geetha Sitaraman, Trae Claar +1
The success of AI is based on the availability of data to train models. While in some cases a single data custodian may have sufficient data to enable AI, often multiple custodians…
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.…
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…
CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources
Sikha Pentyala, Mayana Pereira, Martine De Cock
Data is the lifeblood of the modern world, forming a fundamental part of AI, decision-making, and research advances. With increase in interest in data, governments have taken impor…
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…
Secure Multiparty Computation for Synthetic Data Generation from Distributed Data
Mayana Pereira, Sikha Pentyala, Anderson Nascimento +2
Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education. Synthetic data generation algori…
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning
Sikha Pentyala, Nicola Neophytou, Anderson Nascimento +2
Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute suc…
PrivFair: a Library for Privacy-Preserving Fairness Auditing
Sikha Pentyala, David Melanson, Martine De Cock +1
Machine learning (ML) has become prominent in applications that directly affect people's quality of life, including in healthcare, justice, and finance. ML models have been found t…
Training Differentially Private Models with Secure Multiparty Computation
Sikha Pentyala, Davis Railsback, Ricardo Maia +4
We address the problem of learning a machine learning model from training data that originates at multiple data owners while providing formal privacy guarantees regarding the prote…
Privacy-Preserving Training of Tree Ensembles over Continuous Data
Samuel Adams, Chaitali Choudhary, Martine De Cock +5
Most existing Secure Multi-Party Computation (MPC) protocols for privacy-preserving training of decision trees over distributed data assume that the features are categorical. In re…
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…