Special Issue "Cryptology and Information Security in Open and Convergent Environment"

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 (20 January 2023) | Viewed by 3396

Special Issue Editors

Prof. Dr. Shaojing Fu
E-Mail Website
Guest Editor
College of Computer, National University of Defense Technology, Changsha 410073, China
Interests: applied cryptography; security and privacy issues for cloud computing
Dr. Yuchuan Luo
E-Mail Website
Guest Editor
College of Computer, National University of Defense Technology, Changsha 410073, China
Interests: data security and privacy; confidential computing; privacy-enhanced technologies for cloud computing and IoT

Special Issue Information

Dear Colleagues,

With the rapid integration and development of mobile Internet, cloud computing, big data, artificial intelligence and the Internet of things, the demand for data security protection is becoming more urgent. Cryptography is the most effective and reliable technical way to protect data security. In the open environment, cryptographic applications are facing new challenges. There is a conflict between information retrieval processing and encryption protection and the storage and computing resources available for cryptographic operations are limited in special environments.

To address these severe increasing security challenges and meet the demand for data protection, this Special Issue is interested in inviting and gathering recent advanced cryptology and information security theories, methods, and techniques.

Prof. Dr. Shaojing Fu
Dr. Yuchuan Luo
Guest Editors

Manuscript Submission Information

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Keywords

  • applied cryptography
  • cryptographic protocols
  • network security and trust
  • confidential computing
  • privacy-enhanced technologies
  • computation outsourcing security
  • blockchain
  • trustworthy Machine learning
  • security and privacy in crowdsourcing
  • mobile crowdsensing
  • cloud security

 

Published Papers (5 papers)

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Research

Article
A Fuzzy-Based Co-Incentive Trust Evaluation Scheme for Edge Computing in CEEC Environment
Appl. Sci. 2022, 12(23), 12453; https://doi.org/10.3390/app122312453 - 05 Dec 2022
Viewed by 342
Abstract
With the development of 5G and artificial intelligence, the security of Cloud-Edge-End Collaboration (CEEC) networks becomes an increasingly prominent issue due to the complexity of the environment, real-time variability and diversity of edge devices in CEEC networks. In this paper, we design a [...] Read more.
With the development of 5G and artificial intelligence, the security of Cloud-Edge-End Collaboration (CEEC) networks becomes an increasingly prominent issue due to the complexity of the environment, real-time variability and diversity of edge devices in CEEC networks. In this paper, we design a lightweight fuzzy collaborative trust evaluation model (LFCTEM) for edge devices, and calculate the trust values of edge devices by fuzzifying trust factors. To alleviate the selfish behavior of edge devices, this paper introduces an incentive mechanism in the trust evaluation model, and achieves a long-term incentive effect by designing an incentive negative decay mechanism, which enhances the initiative of collaboration and improves the interference resistance of CEEC networks. We verify the performance of LFCTEM through simulation experiments. Compared with other methods, our model enhances the detection rate of malicious edge devices by 19.11%, which improves the reliability of the CEEC trust environment. Meanwhile, our model reduces the error detection rate of edge devices by 16.20%, thus alleviating error reporting of the CEEC trust environment. Full article
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Article
DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
Appl. Sci. 2022, 12(21), 11118; https://doi.org/10.3390/app122111118 - 02 Nov 2022
Viewed by 485
Abstract
Collaborative filtering is a popular approach for building an efficient and scalable recommender system. However, it has not unleashed its full potential due to the following problems. (1) Serious privacy concerns: collaborative filtering relies on aggregated user data to make personalized predictions, which [...] Read more.
Collaborative filtering is a popular approach for building an efficient and scalable recommender system. However, it has not unleashed its full potential due to the following problems. (1) Serious privacy concerns: collaborative filtering relies on aggregated user data to make personalized predictions, which means that the centralized server can access and compromise user privacy. (2) Expensive resources required: conventional collaborative filtering techniques require a server with powerful computing capacity and large storage space, so that the server can train and maintain the model. (3) Considering only one form of user feedback: most existing works aim to model user preferences based on explicit feedback (e.g., ratings) or implicit feedback (e.g., purchase history, viewing history) due to their heterogeneous representation; however, these two forms of feedback are abundant in most collaborative filtering applications, can both affect the model, and very few works studied the simultaneous use thereof. To solve the above problems, in this study we focus on implementing decentralized probabilistic matrix factorization for privacy-preserving recommendations. First, we explore the existing collaborative filtering algorithms and propose a probabilistic matrix co-factorization model. By integrating explicit and implicit feedback into a shared probabilistic model, the model can cope with the heterogeneity between these two forms of feedback. Further, we devise a decentralized learning method that allows users to keep their private data on the end devices. A novel decomposing strategy is proposed for users to exchange only non-private information, in which stochastic gradient descent is used for updating the models. Complexity analysis proves that our method is highly efficient with linear computation and communication complexity. Experiments conducted on two real-world datasets FilmTrust and Epinions show that our model gains a guarantee of convergence as the RMSE decreases quickly within 100 rounds of iterations. Compared with the state-of-the-art models, our model achieves lower model loss in rating prediction task and higher precision in item recommendation task. Full article
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Article
Research on Side-Channel Analysis Based on Deep Learning with Different Sample Data
Appl. Sci. 2022, 12(16), 8246; https://doi.org/10.3390/app12168246 - 18 Aug 2022
Cited by 1 | Viewed by 564
Abstract
With the in-depth integration of deep learning and side-channel analysis (SCA) technology, the security threats faced by embedded devices based on the Internet of Things (IoT) have become increasingly prominent. By building a neural network model as a discriminator, the correlation between the [...] Read more.
With the in-depth integration of deep learning and side-channel analysis (SCA) technology, the security threats faced by embedded devices based on the Internet of Things (IoT) have become increasingly prominent. By building a neural network model as a discriminator, the correlation between the side information leaked by the cryptographic device, the key of the cryptographic algorithm, and other sensitive data can be explored. Then, the security of cryptographic products can be evaluated and analyzed. For the AES-128 cryptographic algorithm, combined with the CW308T-STM32F3 demo board on the ChipWhisperer experimental platform, a Correlation Power Analysis (CPA) is performed using the four most common deep learning methods: the multilayer perceptron (MLP), the convolutional neural network (CNN), the recurrent neural network (RNN), and the long short-term memory network (LSTM) model. The performance of each model is analyzed in turn when the samples are small data sets, sufficient data sets, and data sets of different scales. Finally, each model is comprehensively evaluated by indicators such as classifier accuracy, network loss, training time, and rank of side-channel attacks. The experimental results show that the convolutional neural network CNN classifier has higher accuracy, lower loss, better robustness, stronger generalization ability, and shorter training time. The rank value is 2, that is, only two traces can recover the correct key byte information. The comprehensive performance effect is better. Full article
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Article
SRP: A Microscopic Look at the Composition Mechanism of Website Fingerprinting
Appl. Sci. 2022, 12(15), 7937; https://doi.org/10.3390/app12157937 - 08 Aug 2022
Viewed by 599
Abstract
Tor serves better at protecting users’ privacy than other anonymous communication tools. Even though it is resistant to deep packet inspection, Tor can be de-anonymized by the website fingerprinting (WF) attack, which aims to monitor the website users are browsing. WF attacks based [...] Read more.
Tor serves better at protecting users’ privacy than other anonymous communication tools. Even though it is resistant to deep packet inspection, Tor can be de-anonymized by the website fingerprinting (WF) attack, which aims to monitor the website users are browsing. WF attacks based on deep learning perform better than those using manually designed features and traditional machine learning. However, a deep learning model is data-hungry when simulating the mapping relations of traffic and the website it belongs to, which may not be practical in reality. In this paper, we focus on investigating the composition mechanism of website fingerprinting and try to solve data shortage with bionic traffic traces. More precisely, we propose a new concept called the send-and-receive pair (SRP) to deconstruct traffic traces and design SRP-based cumulative features. We further reconstruct and generate bionic traces (BionicT) based on the rearranged SRPs. The results show that our bionic traces can improve the performance of the state-of-the-artdeep-learning-based Var-CNN. The increment in accuracy reaches up to 50% in the five-shot setting, much more effective than the data augmentation method HDA. In the 15/20-shot setting, our method even defeated TF with more than 95% accuracy in closed-world scenarios and an F1-score of over 90% in open-world scenarios. Moreover, expensive experiments show that our method can enhance the deep learning model’s ability to combat concept drift. Overall, the SRP can serve as an effective tool for analyzing and describing website traffic traces. Full article
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Article
A Novel Image Encryption Algorithm Based on Voice Key and Chaotic Map
Appl. Sci. 2022, 12(11), 5452; https://doi.org/10.3390/app12115452 - 27 May 2022
Cited by 1 | Viewed by 693
Abstract
This paper proposes a new image encryption algorithm. First, time-domain and frequency-domain features of the user’s voice are extracted to generate a voice key. Second, the key is iterated through a chaotic map multiple times to map the key data to the chaotic [...] Read more.
This paper proposes a new image encryption algorithm. First, time-domain and frequency-domain features of the user’s voice are extracted to generate a voice key. Second, the key is iterated through a chaotic map multiple times to map the key data to the chaotic oscillation region, and, subsequently, the parameters of the oscillation area are used to encrypt the user’s image. Third, at the time of decryption, the user’s latest voice data are re-extracted to generate a new voice key and decrypt the encrypted image. The encrypted image cannot be successfully decrypted if there are differences between the two extracted voices in the time or frequency domain. Finally, the experiments are performed using 80 groups of face images and voice data, all of which pass the encryption and decryption experiments. In addition, various safety tests have been carried out on the algorithm. The key sensitivity of the algorithm is verified by the normalized cross-correlation parameter Cncc. The effective anti-attack ability of the algorithm is verified by measuring the correlation between adjacent pixels, the number of changing pixel rate (NPCR) and the unified averaged changed intensity (UACI). The key space of the proposed algorithm is greater than 2100, and it has good anti-cracking ability. Full article
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