822 citations · 2.4k across the 23 of their papers we have counts for
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Marginal Release Under Local Differential Privacy
Tejas Kulkarni, Graham Cormode, Divesh Srivastava
Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subject…
Self-Supervised Intrinsic Image Decomposition
Michael Janner, Jiajun Wu, Tejas D. Kulkarni +2
Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data. In contrast to traditional fully supervis…
Constrained Differential Privacy for Count Data
Graham Cormode, Tejas Kulkarni, Divesh Srivastava
Concern about how to aggregate sensitive user data without compromising individual privacy is a major barrier to greater availability of data. The model of differential privacy has…