Smooth sensitivity and sampling in private data analysis
- Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith
- Computer ScienceSymposium on the Theory of Computing
- 11 June 2007
This is the first formal analysis of the effect of instance-based noise in the context of data privacy, and shows how to do this efficiently for several different functions, including the median and the cost of the minimum spanning tree.
What Can We Learn Privately?
- S. Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith
- Computer Science49th Annual IEEE Symposium on Foundations of…
- 6 March 2008
This work investigates learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in the contexts where aggregate information is released about a database containing sensitive information about individuals.
Analyzing Graphs with Node Differential Privacy
- S. Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith
- Computer ScienceTheory of Cryptography Conference
- 3 March 2013
A generic, efficient reduction is derived that allows us to apply any differentially private algorithm for bounded-degree graphs to an arbitrary graph, based on analyzing the smooth sensitivity of the 'naive' truncation that simply discards nodes of high degree.
Private Analysis of Graph Structure
- Vishesh Karwa, Sofya Raskhodnikova, Adam D. Smith, G. Yaroslavtsev
- Computer Science, MathematicsTODS
- 7 October 2014
This work extends the approach of Nissim et al. to a new class of statistics, namely k-star queries, and gives hardness results indicating that the approach used for triangles cannot easily be extended to k-triangles.
Monotonicity testing over general poset domains
- E. Fischer, E. Lehman, I. Newman, Sofya Raskhodnikova, R. Rubinfeld, Alex Samorodnitsky
- Mathematics, Computer ScienceSymposium on the Theory of Computing
- 19 May 2002
It is shown that in its most general setting, testing that Boolean functions are close to monotone is equivalent, with respect to the number of required queries, to several other testing problems in logic and graph theory.
Some 3CNF properties are hard to test
- Eli Ben-Sasson, P. Harsha, Sofya Raskhodnikova
- Mathematics, Computer ScienceSymposium on the Theory of Computing
- 9 June 2003
It is proved that there are 3CNF properties that require a linear number of queries, even for adaptive tests, which contrasts with 2C NF properties that are testable with O(√n) queries.
Strong Lower Bounds for Approximating Distribution Support Size and the Distinct Elements Problem
- Sofya Raskhodnikova, D. Ron, Amir Shpilka, Adam D. Smith
- Computer ScienceIEEE Annual Symposium on Foundations of Computer…
- 21 October 2007
The problem of approximating the support size of a distribution from a small number of samples, when each element in the distribution appears with probability at least 1/n is considered, and a nearly linear in n lower bound on the query complexity is proved.
Approximation algorithms for spanner problems and Directed Steiner Forest
- P. Berman, Arnab Bhattacharyya, K. Makarychev, Sofya Raskhodnikova, G. Yaroslavtsev
- Mathematics, Computer ScienceInformation and Computation
- 2013
Lipschitz Extensions for Node-Private Graph Statistics and the Generalized Exponential Mechanism
- Sofya Raskhodnikova, Adam D. Smith
- Mathematics, Computer ScienceIEEE Annual Symposium on Foundations of Computer…
- 1 October 2016
The authors' Lipschitz extensions and the generalized exponential mechanism are used to design a node differentially private algorithm for approximating the degree distribution of a sensitive graph, which is much more accurate than those from previous work.
Transitive-Closure Spanners
- Arnab Bhattacharyya, Elena Grigorescu, Kyomin Jung, Sofya Raskhodnikova, David P. Woodruff
- Computer Science, MathematicsSIAM journal on computing (Print)
- 12 August 2008
The common task implicitly tackled in these diverse applications as the problem of constructing sparse TC-spanners is abstracted asThe study of approximability of the size of the sparsest of a given directed graph is initiated.
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