Related papers: Secure Linear Programming Using Privacy-Preserving…

This paper has been withdrawn by the author due to a crucial sign error in equation 1

This paper has been withdrawn by the author, due to necessity of revision.

Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potential gains are highly anticipated if different organizations…

This paper has been withdrawn by the author. It will be published again after submission to a journal.

This paper has been withdrawn by the author due to a crucial sign error in equation 1

This paper has been withdrawn by the authors due to some fatal errors in the analysis.

A critically important component of most signal processing procedures is that of computing the distance between signals. In multi-party processing applications where these signals belong to different parties, this introduces privacy…

This paper has been temporarily withdrawn by the author(s),

This preprint has been withdrawn. It is because I will never publish this preprint since everything has been contained in my new preprint: arXiv:0907.1506. Please refer to arXiv:0907.1506. Please do not cite this preprint any more.

Large language models (LLMs) have significantly transformed natural language understanding and generation, but they raise privacy concerns due to potential exposure of sensitive information. Studies have highlighted the risk of information…

This paper has been withdrawn by the authors, due the copyright policy of the journal it has been submited to.

The author decided to withdraw this paper by 1) an error in Lemma 5.11 (and 5.12) which requires some justification; 2) the main result of this paper suffers overlap with arXiv:1203.5254; 3) the author decided to split arXiv:1203.5254 into…

This paper has been withdrawn by the author(s), due to double submission. You can find it under: physics/0208019

This paper has been withdrawn by the authors due to an error first noted by M. Lukin.

This paper has been withdrawn by the author due to a crucial sign error in equation 2.

This paper considers the single-server Private Linear Transformation (PLT) problem with individual privacy guarantees. In this problem, there is a user that wishes to obtain $L$ independent linear combinations of a $D$-subset of messages…

This paper has been withdrawn and replaced by quant-ph/0609207.

Privacy-preserving scalar product (PPSP) protocols are an important building block for secure computation tasks in various applications. Lu et al. (TPDS'13) introduced a PPSP protocol that does not rely on cryptographic assumptions and that…

In this paper, a privacy preserving image classification method is proposed under the use of ConvMixer models. To protect the visual information of test images, a test image is divided into blocks, and then every block is encrypted by using…

With the increasing demands for privacy protection, many privacy-preserving machine learning systems were proposed in recent years. However, most of them cannot be put into production due to their slow training and inference speed caused by…