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This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints. Unlike standard noise-additive algorithms, that act…
This paper has been withdrawn by the author due to a gap in the proof of the main result.
This paper has been withdrawn by the author.
Erroneous submission in violation of copyright removed by arXiv admin.
The general structure of the paper should be remaid. Hence author removed this paper from arXiv.
This paper has been withdrawn by the author due to a crucial error in the proof of Theorem 1.
This paper has been withdrawn by the author, due a crucial mistake in proof of lemma 4.2.
This paper has been withdrawn by the author due to errors.
This paper has been withdrawn by the author due to serious flaws in certain proofs. For instance, the method used to construct certain automorphic representations is flawed.
This paper has been withdrawn by the author. It will be replaced, substantially modified, by sections of the author's completed PhD thesis.
This paper has been withdrawn by the author due to an error.
This paper has been withdrawn by the author due to an error in the derivation.
This paper has been withdrawn
This paper has been withdrawn at the author's request.
This paper has been withdrawn by the author, due to a crucial error in the proof of Thm.1
This paper has been withdrawn.
Paper withdrawn by the author
This paper has been withdrawn.
This paper has been withdrawn by the author, due to a crucial error in page 5.
This paper has been withdrawn by the author, due an error in the proof of Proposion 2.13.