{"title":"IWCIT 2020 Copyright Page","authors":"","doi":"10.1109/iwcit50667.2020.9163462","DOIUrl":"https://doi.org/10.1109/iwcit50667.2020.9163462","url":null,"abstract":"","PeriodicalId":360380,"journal":{"name":"2020 Iran Workshop on Communication and Information Theory (IWCIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131947688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi Variable-layer Neural Networks for Decoding Linear Codes","authors":"Samira Malek, Saber Salehkaleybar, A. Amini","doi":"10.1109/iwcit50667.2020.9163473","DOIUrl":"https://doi.org/10.1109/iwcit50667.2020.9163473","url":null,"abstract":"The belief propagation algorithm is a state of the art decoding technique for a variety of linear codes such as LDPC codes. The iterative structure of this algorithm is reminiscent of a neural network with multiple layers. Indeed, this similarity has been recently exploited to improve the decoding performance by tuning the weights of the equivalent neural network. In this paper, we introduce a new network architecture by increasing the number of variable-node layers, while keeping the check-node layers unchanged. The changes are applied in a manner that the decoding performance of the network becomes independent of the transmitted codeword; hence, a training stage with only the all-zero codeword shall be sufficient. Simulation results on a number of well-studied linear codes, besides an improvement in the decoding performance, indicate that the new architecture is also simpler than some of the existing decoding networks.","PeriodicalId":360380,"journal":{"name":"2020 Iran Workshop on Communication and Information Theory (IWCIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115515418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"IWCIT 2020 Cover Page","authors":"","doi":"10.1109/iwcit50667.2020.9163532","DOIUrl":"https://doi.org/10.1109/iwcit50667.2020.9163532","url":null,"abstract":"","PeriodicalId":360380,"journal":{"name":"2020 Iran Workshop on Communication and Information Theory (IWCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128667940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seyed Reza Hoseini Najarkolaei, M. Maddah-ali, M. Aref
{"title":"Coded Secure Multi-Party Computation for Massive Matrices with Adversarial Nodes","authors":"Seyed Reza Hoseini Najarkolaei, M. Maddah-ali, M. Aref","doi":"10.1109/IWCIT50667.2020.9163533","DOIUrl":"https://doi.org/10.1109/IWCIT50667.2020.9163533","url":null,"abstract":"In this work1, we consider the problem of secure multi-party computation (MPC), consisting of F sources, each has access to a large private matrix, N processing nodes or workers, and one master. The master is interested in the result of a polynomial function of the input matrices. Each source sends a randomized functions of its matrix, called as its share, to each server. The workers process their shares in interaction with each other, and send some results to the master such that it can derive the final results. There are several constraints: (1) each worker has a constraint on its storage, such that it can store equivalent of $displaystyle frac{1}{m}$ fraction of size of each input matrices from each source, information about the private inputs or can do malicious actions to make the final result incorrect. The objective is to design an MPC scheme with the minimum number of the workers, called recovery threshold, such that the final result is correct, servers learn no information about the input matrices, and the master learns nothing beyond the final result. In this paper, we propose an MPC scheme that achieves the recovery threshold of 3t+2m-1 workers, which is order-wise less than the recovery threshold of the conventional methods. The main challenge is to manage the errors propagated through the network by the adversarial nodes when the workers interact with each other in each round.","PeriodicalId":360380,"journal":{"name":"2020 Iran Workshop on Communication and Information Theory (IWCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128996797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}