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Applied Sciences
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Cryptography in Data Protection and Privacy-Enhancing Technologies
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Cryptography in Data Protection and Privacy-Enhancing Technologies

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

Deadline for manuscript submissions: 20 October 2024 | Viewed by 1770

Special Issue Editors

Dr. Yi-Fan Tseng

E-Mail Website
Guest Editor
Department of Computer Science, National Chenchi University, Taipei 11605, Taiwan
Interests: public key cryptography; provable security; cryptographic protocol
Prof. Dr. Chun-I Fan

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
Interests: applied cryptology; information and communication security; AI security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the age of rapid technological advancement, protecting personal information is of paramount importance. Privacy-Enhancing Technologies (PETs) stand as the forefront guardians, embodying fundamental data protection principles. By minimizing personal data use, maximizing data security, and empowering individuals, PETs play a pivotal role in preserving privacy in our interconnected world. These technologies empower online users to safeguard the privacy of their personally identifiable information (PII) during interactions with various services and applications, utilizing innovative techniques to reduce the possession of personal data by information systems without compromising functionality.

This Special Issue delves into the multifaceted realm of Privacy-Enhancing Technologies, exploring their impact on the digital landscape. Topics of interest include, but are not limited to, the following:

  • Cryptographic protocols for secure data transmission;
  • Encryption algorithms and techniques in privacy protection;
  • Blockchain technology for enhancing data privacy;
  • Authentication and verification methods for secure access;
  • Digital signatures in ensuring data integrity and authenticity;
  • Multiparty computation.

Dr. Yi-Fan Tseng
Prof. Dr. Chun-I Fan
Guest Editors

Manuscript Submission Information

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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

  • privacy-enhancing technologies (PETs)
  • data protection
  • cryptographic protocols
  • multiparty computation
  • digital signatures
  • blockchain technology

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Published Papers (2 papers)

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Research

20 pages, 1301 KiB  
Article
A Reliable Aggregation Method Based on Threshold Additive Secret Sharing in Federated Learning with Quality of Service (QoS) Support
by Yu-Ting Ting, Po-Wen Chi and Chien-Ting Kuo
Appl. Sci. 2024, 14(19), 8959; https://doi.org/10.3390/app14198959 - 4 Oct 2024
Viewed by 350
Abstract
Federated learning is a decentralized privacy-preserving mechanism that allows multiple clients to collaborate without exchanging their datasets. Instead, they jointly train a model by uploading their own gradients. However, recent research has shown that attackers can use clients’ gradients to reconstruct the original [...] Read more.
Federated learning is a decentralized privacy-preserving mechanism that allows multiple clients to collaborate without exchanging their datasets. Instead, they jointly train a model by uploading their own gradients. However, recent research has shown that attackers can use clients’ gradients to reconstruct the original training data, compromising the security of federated learning. Thus, there has been an increasing number of studies aiming to protect gradients using different techniques. One common technique is secret sharing. However, it has been shown in previous research that when using secret sharing to protect gradient privacy, the original gradient cannot be reconstructed when one share is lost or a server is damaged, causing federated learning to be interrupted. In this paper, we propose an approach that involves using additive secret sharing for federated learning gradient aggregation, making it difficult for attackers to easily access clients’ original gradients. Additionally, our proposed method ensures that any server damage or loss of gradient shares are unlikely to impact the federated learning operation, within a certain probability. We also added a membership level system, allowing members of varying levels to ultimately obtain models with different accuracy levels. Full article
(This article belongs to the Special Issue Cryptography in Data Protection and Privacy-Enhancing Technologies)
►▼ Show Figures

Figure 1

Figure 1
<p><span class="html-italic">t</span>-out-of-<span class="html-italic">n</span> federated learning architecture diagram. In this example, <span class="html-italic">t</span> is 2 and <span class="html-italic">n</span> is 4. After training the model locally, the client divides the gradients into 4 shares and distributes them to all servers. Each server aggregates the received shares. The selected 2 servers, the leftmost server and the rightomst server here, then return the aggregated shares to the client, which aggregates the <span class="html-italic">t</span> received shares to update the model.</p> Full article ">Figure 2
<p><math display="inline"><semantics> <msub> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">i</mi> </msub> </semantics></math> splits the gradients after local training and sends the gradients and data volume to <math display="inline"><semantics> <msub> <mi>Svr</mi> <mi mathvariant="normal">s</mi> </msub> </semantics></math>.</p> Full article ">Figure 3
<p>From <span class="html-italic">n</span> servers, select <span class="html-italic">t</span> servers, and calculate the weighted average of the selected <span class="html-italic">t</span> servers.</p> Full article ">Figure 4
<p>All clients update the model after receiving <span class="html-italic">w</span> from the <span class="html-italic">t</span> servers.</p> Full article ">Figure 5
<p>Process diagram for providing models of different accuracy to clients of varying levels.</p> Full article ">Figure 6
<p>Comparison of the accuracy of our proposed method with FedAvg [<a href="#B2-applsci-14-08959" class="html-bibr">2</a>] and FedShare [<a href="#B8-applsci-14-08959" class="html-bibr">8</a>] using the IID MNIST [<a href="#B29-applsci-14-08959" class="html-bibr">29</a>] dataset with 5 and 10 servers and varying numbers of clients.</p> Full article ">Figure 7
<p>Comparison of the accuracy of our proposed method with FedAvg [<a href="#B2-applsci-14-08959" class="html-bibr">2</a>] and FedShare [<a href="#B8-applsci-14-08959" class="html-bibr">8</a>] using the IID Fashion-MNIST [<a href="#B30-applsci-14-08959" class="html-bibr">30</a>] dataset with 5 and 10 servers and varying numbers of clients.</p> Full article ">Figure 8
<p>Comparison of the accuracy of our proposed method with FedAvg [<a href="#B2-applsci-14-08959" class="html-bibr">2</a>] and FedShare [<a href="#B8-applsci-14-08959" class="html-bibr">8</a>] using the Non-IID MNIST [<a href="#B29-applsci-14-08959" class="html-bibr">29</a>] and Non-IID Fashion-MNIST [<a href="#B30-applsci-14-08959" class="html-bibr">30</a>] datasets with 5 servers and varying numbers of clients.</p> Full article ">Figure 9
<p>Comparison of the average execution time using the IID MNIST [<a href="#B29-applsci-14-08959" class="html-bibr">29</a>] and IID FashionMNIST [<a href="#B30-applsci-14-08959" class="html-bibr">30</a>] datasets for different numbers of clients and servers with FedAvg [<a href="#B2-applsci-14-08959" class="html-bibr">2</a>], FedShare [<a href="#B8-applsci-14-08959" class="html-bibr">8</a>], and our method.</p> Full article ">Figure 10
<p>Comparison of the average execution time using the Non-IID MNIST [<a href="#B29-applsci-14-08959" class="html-bibr">29</a>] and Non-IID FashionMNIST [<a href="#B30-applsci-14-08959" class="html-bibr">30</a>] datasets for different numbers of clients and servers with FedAvg [<a href="#B2-applsci-14-08959" class="html-bibr">2</a>], FedShare [<a href="#B8-applsci-14-08959" class="html-bibr">8</a>], and our method.</p> Full article ">Figure 11
<p>The probability that the mechanism fails due to the number of damaged servers for 4-out-of-16 and 5-out-of-25 situations.</p> Full article ">Figure 12
<p>The probability that an attacker successfully reconstructs the gradient by attacking <span class="html-italic">d</span> servers for the 4-out-of-16 and 5-out-of-25 situations.</p> Full article ">Figure 13
<p>Box plot of gradient model accuracy at different magnifications (50 times).</p> Full article ">
15 pages, 438 KiB  
Article
Protecting Instant Messaging Notifications against Physical Attacks: A Novel Instant Messaging Notification Protocol Based on Signal Protocol
by Raghad Almari, Abdullah Almosallam, Saleh Almousa and Saad Alahmadi
Appl. Sci. 2024, 14(14), 6348; https://doi.org/10.3390/app14146348 - 21 Jul 2024
Viewed by 957
Abstract
Over the years, there has been a significant surge in the popularity of instant messaging applications (IMAs). However, the message notification functionality in IMAs exhibits certain limitations. Some IMAs fail to alert users about new messages after their phone restarts unless they unlock [...] Read more.
Over the years, there has been a significant surge in the popularity of instant messaging applications (IMAs). However, the message notification functionality in IMAs exhibits certain limitations. Some IMAs fail to alert users about new messages after their phone restarts unless they unlock the phone. This is a consequence of end-to-end encryption (E2EE) and the app not knowing the message is in the queue until the app decrypts it. This approach using E2EE is used to prevent offline attacks, as the key is unavailable to decrypt the notification messages. In this paper, we introduce a novel design and implementation of a message notification protocol for IMAs based on the Signal protocol. The proposed protocol aims to securely display notifications on a locked device and ensures that cryptographic keys are stored in a location that is isolated from the user’s device to prevent offline attacks. This approach enhances the security of private key storage, safeguarding private keys against various external threats. The innovative design strengthens the off-site key management system, rendering it resilient against offline attacks and mitigating the risk of key compromise. Additionally, the proposed protocol is highly efficient, requiring no specialized hardware for implementation. It offers confidentiality of cryptographic keys and protection against offline attacks, further enhancing the overall security of the system. We evaluate the protocol’s effectiveness by analyzing multiple independent implementations that pass a suite of formal tests via ProVerif. Full article
(This article belongs to the Special Issue Cryptography in Data Protection and Privacy-Enhancing Technologies)
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Figure 1

Figure 1
<p>Network model.</p> Full article ">Figure 2
<p>Notification database.</p> Full article ">Figure 3
<p>Symmetric ratchet.</p> Full article ">Figure 4
<p>Asymmetric ratchet.</p> Full article ">Figure 5
<p>ProVerif output.</p> Full article ">Figure 6
<p>ProVerif output (Bob’s name).</p> Full article ">
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