BARC Talk by Tamalika Mukherjee
How to Make Your Approximation Algorithm Private
We develop a framework for efficiently transforming certain approximation algorithms into differentially-private variants, in a black-box manner. Our results focus on algorithms A that output an approximation to a function f of the form (1−a)f(x)−k<=A(x)<=(1+a)f(x)+k, where 0<=a <1 is a parameter that can be "tuned" to small-enough values while incurring only a poly blowup in the running time/space. We show that such algorithms can be made DP without sacrificing accuracy, as long as the function f has small global sensitivity. We achieve these results by applying the smooth sensitivity framework developed by Nissim, Raskhodnikova, and Smith (STOC 2007).
Our framework naturally applies to transform non-private FPRAS (resp. FPTAS) algorithms into (ϵ,δ)-DP (resp. ϵ-DP) approximation algorithms. We apply our framework in the context of sublinear-time and sublinear-space algorithms, while preserving the nature of the algorithm in meaningful ranges of the parameters.
Our results include the first (to the best of our knowledge) (ϵ,δ)-edge DP sublinear-time algorithm for estimating the number of triangles, the number of connected components, and the weight of a MST of a graph, as well as a more efficient algorithm (while sacrificing pure DP in contrast to previous results) for estimating the average degree of a graph. In the area of streaming algorithms, our results include (ϵ,δ)-DP algorithms for estimating L_p-norms, distinct elements, and weighted MST for both insertion-only and turnstile streams.
Our transformation also provides a private version of the smooth histogram framework, which is commonly used for converting streaming algorithms into sliding window variants, and achieves a multiplicative approximation to many problems, such as estimating L_p-norms, distinct elements, and the length of the longest increasing subsequence.