Related papers: Secure Linear Programming Using Privacy-Preserving…

This paper has been withdrawn by the author to comply with the journal policy to which it has been submitted.

This paper has been withdrawn. It was an early draft submitted prematurely in error. A complete version is to be submitted shortly.

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This paper has been withdrawn due to the adherance to the double submission policies of a refereed journal. Our apologies.

This paper has been withdrawn by the author.

This paper has been withdrawn to address an omission. It will be resubmitted in the near future.

Privacy is a fundamental human right. Data privacy is protected by different regulations, such as GDPR. However, modern large language models require a huge amount of data to learn linguistic variations, and the data often contains private…

This paper has been withdrawn by the authors due to an incorrect analysis.

In this paper, we present a new perspective of single server private information retrieval (PIR) schemes by using the notion of linear error-correcting codes. Many of the known single server schemes are based on taking linear combinations…

This paper has been withdrawn.

This paper has been withdrawn because the models on which it was based have undergone significant changes and improvements. A new paper with the same title, based on the improved models, is accepted for publication in MNRAS and is available…

This paper has been withdrawn, as it has been merged into arXiv:1009.6144

This paper has been withdrawn by the author due to a crucial errors.

For privacy concerns to be addressed adequately in current machine learning systems, the knowledge gap between the machine learning and privacy communities must be bridged. This article aims to provide an introduction to the intersection of…

This paper has been withdrawn Abstract: This paper has been withdrawn by the author due to the publication.

This paper was removed by arXiv admin due to 94% plagiarism from uncited reference hep-th/0507153.

this is a duplicate submission(original is arXiv:1612.02141). Hence want to withdraw it

The paper has been withdrawn by change of content and some errors in the examples.

In this paper we present the Sampling Privacy mechanism for privately releasing personal data. Sampling Privacy is a sampling based privacy mechanism that satisfies differential privacy.

This paper has been withdrawn by the author.