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Applied Sciences
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New Trends in Data Security and Privacy Based on Cryptographic...
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New Trends in Data Security and Privacy Based on Cryptographic Techniques

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 August 2023) | Viewed by 1340

Special Issue Editors

Dr. Wei Hu

E-Mail Website
Guest Editor
School of Cybersecurity, Northwestern Polytechnical University, Xian 710072, China
Interests: cryptographic algorithms and applications; cryptanlysis; fromal security verification; hardware security
Special Issues, Collections and Topics in MDPI journals
Dr. Jinhui Liu

E-Mail Website
Guest Editor
School of Cybersecurity, Northwestern Polytechnical University, Xian 710072, China
Interests: cryptographic algorithm design and analysis; blockchain security; data security; cloud computing security

Special Issue Information

Dear Colleagues,

Data security and privacy is a primary concern in new computing paradigms such as cloud, edge, and fog technologies, as well as the Internet of Things (IoT). Cryptographic algorithms and protocols play vital roles in delivering powerful and resilient security and privacy guarantees. This Special Issue aims to present the recent developments and emerging trends in the field of Data Security and Privacy Based on Cryptographic Techniques, particularly theories, applications, and security evaluations.

This Special Issue focuses on the advances in cryptographic techniques for protecting data security and privacy. It will publish high-quality, original research and comprehensive survey papers, including but not limited to the following research topics:

  • Cryptographic algorithms and protocols;
  • Security metrics and models;
  • Security threats and attack vectors;
  • Secure computing architectures;
  • Big data security;
  • Blockchain security;
  • Data security and privacy in cloud computing;
  • Data security and privacy in IoT;
  • Applications of cryptographic techniques for protecting data security and privacy;
  • General literature and taxonomy.

Dr. Wei Hu
Dr. Jinhui Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data security and privacy
  • cryptographic algorithms and protocols
  • security threats and models
  • big data security
  • cloud computing security
  • blockchain
  • artificial intelligence
  • architecture modelling and performance evaluation

Published Papers (1 paper)

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Research

15 pages, 775 KiB  
Article
Improved Neural Differential Distinguisher Model for Lightweight Cipher Speck
by Xiaoteng Yue and Wanqing Wu
Appl. Sci. 2023, 13(12), 6994; https://doi.org/10.3390/app13126994 - 09 Jun 2023
Cited by 1 | Viewed by 988
Abstract
At CRYPTO 2019, Gohr proposed the neural differential distinguisher using the residual network structure in convolutional neural networks on round-reduced Speck32/64. In this paper, we construct a 7-round differential neural distinguisher for Speck32/64, which results in better than Gohr’s work. The details are [...] Read more.
At CRYPTO 2019, Gohr proposed the neural differential distinguisher using the residual network structure in convolutional neural networks on round-reduced Speck32/64. In this paper, we construct a 7-round differential neural distinguisher for Speck32/64, which results in better than Gohr’s work. The details are as follows. Firstly, a new data format (C_r,C_r′,d_l,Cl,Cr,Cl′,Cr′) is proposed for the input data of the differential neural distinguisher, which can help the distinguisher to identify the features of the previous round of ciphertexts in the Speck algorithm. Secondly, this paper modifies the convolution layer of the residual block in the residual network, inspired by the Inception module in GoogLeNet. For Speck32/64, the experiments show that the accuracy of the 7-round differential neural distinguisher is 97.13%, which is better than the accuracy of Gohr’s distinguisher of 9.1% and also higher than the currently known accuracy of 89.63%. The experiments also show that the data format and neural network in this paper can improve the accuracy of the distinguisher by 2.38% and 2.1%, respectively. Finally, to demonstrate the effectiveness of the distinguisher in this paper, a key recovery attack is performed on 8-rounds of Speck32/64. The results show that the success rate of recovering the correct key is 92%, with no more than two incorrect bits. Finally, this paper briefly discussed the effect of the number of ciphertext pairs in a sample on the training results of the differential neural distinguisher. When the total number of ciphertext pairs is kept constant, the accuracy of the distinguisher increases with s, but it also leads to the occurrence of overfitting. Full article
(This article belongs to the Special Issue New Trends in Data Security and Privacy Based on Cryptographic Techniques)
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