Jalaj Upadhyay
On Differentially Private Graph Sparsification and Applications
Raman Arora, Jalaj Upadhyay
In this paper, we study private sparsification of graphs. In particular, we give an algorithm that given an input graph, returns a sparse graph which approximates the spectrum of the input graph while ensuring differential privacy. This allows one to solve many graph problems privately yet efficiently and accurately. This is exemplified with application of the proposed meta-algorithm to graph algorithms for privately answering cut-queries, as well as practical algorithms for computing MAX-CUT and SPARSEST-CUT with better accuracy than previously known. We also give an efficient private algorithm to learn Laplacian eigenmap on a graph.
The Price of Privacy for Low-rank Factorization
Jalaj Upadhyay
Neural Information Processing Systems http://nips.cc/
On Differentially Private Graph Sparsification and Applications
Raman Arora, Jalaj Upadhyay
In this paper, we study private sparsification of graphs. In particular, we give an algorithm that given an input graph, returns a sparse graph which approximates the spectrum of the input graph while ensuring differential privacy. This allows one to solve many graph problems privately yet efficiently and accurately. This is exemplified with application of the proposed meta-algorithm to graph algorithms for privately answering cut-queries, as well as practical algorithms for computing MAX-CUT and SPARSEST-CUT with better accuracy than previously known. We also give an efficient private algorithm to learn Laplacian eigenmap on a graph.
Differentially Private Robust Low-Rank Approximation
Raman Arora, Vladimir braverman, Jalaj Upadhyay
We study extensions to the streaming setting where entries of the matrix arrive in an arbitrary order and output is produced at the very end or continually.
Differentially Private Robust Low-Rank Approximation
Raman Arora, Vladimir braverman, Jalaj Upadhyay
Neural Information Processing Systems http://nips.cc/