activity
20192022
most citedNot one but many Tradeoffs: Privacy Vs. Utility in Differentially Private Machine Learning

27 citations · 57 across the 8 of their papers we have counts for

collaborators

13 papers

cs.HC2022

DDoD: Dual Denial of Decision Attacks on Human-AI Teams

Benjamin Tag, Niels van Berkel, Sunny Verma +5

Artificial Intelligence (AI) systems have been increasingly used to make decision-making processes faster, more accurate, and more efficient. However, such systems are also at cons…

cs.CR20221 cited

Unintended Memorization and Timing Attacks in Named Entity Recognition Models

Rana Salal Ali, Benjamin Zi Hao Zhao, Hassan Jameel Asghar +3

Named entity recognition models (NER), are widely used for identifying named entities (e.g., individuals, locations, and other information) in text documents. Machine learning base…

cs.SI2022

A deep dive into the consistently toxic 1% of Twitter

Hina Qayyum, Benjamin Zi Hao Zhao, Ian D. Wood +3

Misbehavior in online social networks (OSN) is an ever-growing phenomenon. The research to date tends to focus on the deployment of machine learning to identify and classify types…

cs.CL2021

Hidden Backdoors in Human-Centric Language Models

Shaofeng Li, Hui Liu, Tian Dong +4

Natural language processing (NLP) systems have been proven to be vulnerable to backdoor attacks, whereby hidden features (backdoors) are trained into a language model and may only…

cs.LG2021

On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models

Benjamin Zi Hao Zhao, Aviral Agrawal, Catisha Coburn +5

With an increase in low-cost machine learning APIs, advanced machine learning models may be trained on private datasets and monetized by providing them as a service. However, priva…

cs.CR2021

Oriole: Thwarting Privacy against Trustworthy Deep Learning Models

Liuqiao Chen, Hu Wang, Benjamin Zi Hao Zhao +2

Deep Neural Networks have achieved unprecedented success in the field of face recognition such that any individual can crawl the data of others from the Internet without their expl…