2 citations · 2 across the 4 of their papers we have counts for
4 papers
Dependency Triad: A Metric to Quantify the Dependencies Between Attributes for Local Differential Privacy
Sandaru Jayawardana, Sennur Ulukus, Ming Ding +1
Collecting multidimensional user data is essential for extracting rich insights across various applications. Local Differential Privacy (LDP) has emerged as a de facto standard for…
The Hidden Cost of Correlation: Rethinking Privacy Leakage in Local Differential Privacy
Sandaru Jayawardana, Sennur Ulukus, Ming Ding +1
Local differential privacy (LDP) has emerged as a promising paradigm for privacy-preserving data collection in distributed systems, where users contribute multi-dimensional records…
Privacy at a Price: Exploring its Dual Impact on AI Fairness
Mengmeng Yang, Ming Ding, Youyang Qu +3
The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maint…
Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy
Songjie Xie, Youlong Wu, Jiaxuan Li +2
As machine learning (ML) becomes more prevalent in human-centric applications, there is a growing emphasis on algorithmic fairness and privacy protection. While previous research h…