Related papers: Privacy constraints in regularized convex optimiza…

This paper has been withdrawn, as it has been merged into arXiv:1009.6144

This paper has been withdrawn by the author due to an error

This paper has been withdrawn

This paper has been withdrawn by the author.

Section 1.3 was incorrect, and 2.1 will be removed from further submissions. A rewritten version will be posted in the future.

This paper has been withdrawn by the author, due to an error in Proposition 2.2.

This results in this paper have been merged with the result in arXiv:1002.3763v1 The authors would like to withdraw this version. Please see arXiv:1008.5356v1 for the merged version.

This paper has been withdrawn by the author, due to the insecurity against attacks received in quant-ph/0605027v5.

This paper has been withdrawn by the author due to an error in the proof of Proposition 4.8.

This paper has been withdrawn by the authors.

This paper was withdrawn by arXiv administrators. It is an erroneous duplicate submission of math.NA/0405095.

We propose a new framework for differentially private optimization of convex functions which are Lipschitz in an arbitrary norm $\|\cdot\|$. Our algorithms are based on a regularized exponential mechanism which samples from the density…

This paper is withdrawn because of an error in Lemma 3.1

This paper has been withdrawn.

A modified version of this paper is under process and with a new title and abstract. Hence, this version of the article is completely withdrawn.

This paper has been withdrawn by the author due to text overlap with arXiv:1102.5004, as well as omission of proper citations to arXiv:1110.4655 and arXiv:1111.0313

We study the task of $(\epsilon, \delta)$-differentially private online convex optimization (OCO). In the online setting, the release of each distinct decision or iterate carries with it the potential for privacy loss. This problem has a…

This paper has been withdrawn by the author due to similarity to the author's other paper

In this paper, we revisit the problem of private stochastic convex optimization. We propose an algorithm based on noisy mirror descent, which achieves optimal rates both in terms of statistical complexity and number of queries to a…

The paper has been withdrawn.