Related papers: A polynomial axles-detection algorithm for a four-…

This paper has been withdrawn by the authors due to a crucial gap in the estimates for m>=4.

The general structure of the paper should be remaid. Hence author removed this paper from arXiv.

Paper has been withdrawn.

This paper has been withdrawn due to its publication

This paper has been withdrawn by the corresponding author because the newest version is now published in Journal of Discrete Algorithms.

Optimal transport (OT) is a framework that can guide the design of efficient resource allocation strategies in a network of multiple sources and targets. To ease the computational complexity of large-scale transport design, we first develop…

This paper have been withdraw by the autors, because of a too early submission.

This submission has been removed by arXiv administrators due to copyright infringement.

In this paper, we propose a polynomial-time algorithm to test whether a given graph contains a subdivision of $K_4$ as an induced subgraph.

This submission has been withdrawn by arXiv administrators because of inappropriate authorship claims.

This paper has been withdrawn by the author due to the presented idea is wrong.

This paper has been withdrawn since it contains some discrepancy with othe authers's recent result. We will not post this until this discrepancy is resolved.

This paper has been withdrawn by the author, due to an error in the proof of Theorem 3.8.

This submission has been removed by arXiv administration because it was submitted in violation of copyright by HAL.

Submission withdrawn because the authors erroneously submitted a revised version as a new submission, see nlin.CD/0002028.

In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the…

Federated learning frameworks typically require collaborators to share their local gradient updates of a common model instead of sharing training data to preserve privacy. However, prior works on Gradient Leakage Attacks showed that private…

This paper has been withdrawn by the author(s), due a crucial error on the entanglement of $\Gamma$ registers.

Among the most challenging traffic-analysis attacks to confound are those leveraging the sizes of objects downloaded over the network. In this paper we systematically analyze this problem under realistic constraints regarding the padding…

This work considers computationally efficient privacy-preserving data release. We study the task of analyzing a database containing sensitive information about individual participants. Given a set of statistical queries on the data, we want…