The application of graph analytics to various domains has yielded tremendous societal and
economical benefits in recent years. However, the increasingly widespread adoption of …

We give efficient protocols and matching accuracy lower bounds for frequency estimation in
the local model for differential privacy. In this model, individual users randomize their data …

Random projections offer an appealing and flexible approach to a wide range of large‐scale
statistical problems. They are particularly useful in high‐dimensional settings, where we …

Differentially private statistical estimation has seen a flurry of developments over the last
several years. Study has been divided into two schools of thought, focusing on empirical …

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 …

We undertake a systematic study of sketching a quadratic form: given an nxn matrix A, create
a succinct sketch sk (A) which can produce (without further access to A) a multiplicative (1+ …

The interconnectedness and interdependence of modern graphs are growing ever more
complex, causing enormous resources for processing, storage, communication, and …

We propose a (ϵ, δ)-differentially private mechanism that, given an input graph G with n
vertices and m edges, in polynomial time generates a synthetic graph G'approximating all …

In this paper, we focus on answering queries, in a differentially private manner, on graph
streams. We adopt the sliding window model of privacy, where we wish to perform analysis …

The interconnectedness and interdependence of modern graphs are growing ever more
complex, causing enormous resources for processing, storage, communication, and …