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Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 13693

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Special Issue Editors

Dr. Jialing He

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Guest Editor

College of Computer Science, Chongqing University, Chongqing 400044, China
Interests: machine learning; differential privacy; IoT security

Prof. Zhi Fang

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Guest Editor

Beijing Institute of Computer Technology and Application, Beijing 100854, China
Interests: natural language processing; data processing

Dr. Chunhai Li

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Guest Editor

School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
Interests: data privacy; Internet of Things; blockchains

Special Issue Information

Dear Colleagues,

The widespread adoption of artificial intelligence (AI), blockchain, and cloud technologies necessitates the addressing of security and privacy concerns and appropriate mathematical analysis to ensure the robustness and integrity of computer systems. The integration of AI, blockchain and cloud techniques with formal mathematical analysis opens up new possibilities for enhancing security, privacy, and trust in various applications. However, this integration also introduces novel challenges, such as customized-but-rigorous security analysis under appropriate mathematical hardness assumptions, preserving privacy federated learning on non-independent and identically distributed data, securing smart contracts against malicious participants, etc. Addressing these challenges requires a multidisciplinary approach that combines insights from mathematics, computer science and cybersecurity.

This Special Issue aims to bring together researchers and practitioners to tackle these research frontiers and challenges. It encourages the exploration of innovative methodologies, definitions, frameworks, and practical solutions that integrate AI, blockchain, cloud, security, and privacy, strengthened by formal mathematical analysis. By fostering collaboration between academia and industry, this Special Issue seeks to contribute to the development of secure, privacy-preserving and trustworthy application systems. Topics of interest for this Special Issue include, but are not limited to, the following:

AI-driven threat detection and mitigation in blockchain networks and cloud communications, utilizing statistical methods and machine learning techniques.
Privacy-enhancing cryptography for AI, blockchain, and cloud applications, with a focus on the mathematical foundations of cryptographic protocols and secure multi-party computation.
Scalability and performance optimization in AI, blockchain, and cloud integration, applying mathematical optimization and queuing theory.
Privacy-enhanced identification and identity management on decentralized and centralized platforms, leveraging information theory and differential privacy
Privacy-preserving federated learning in distributed environments, incorporating secure aggregation and homomorphic encryption.
Secure and privacy-aware data sharing in AI, blockchain and cloud
Trust and reputation mechanisms in AI, blockchain and cloud systems.
Security and integrity of blockchain-based smart contracts.
Privacy and security challenges in flexible and scalable applications (e.g., healthcare, finance, supply chain), exploring the mathematical foundations of risk assessment and threat modeling.
Definitions, theorems, and frameworks in provable security and symbolic analysis.

Dr. Jialing He
Prof. Zhi Fang
Dr. Chunhai Li
Guest Editors

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Keywords

privacy-preserving techniques
secure smart contracts in blockchain
AI-driven threat detection
identity management on decentralized platforms
federated learning
privacy-aware data sharing
trust and reputation management
privacy-enhancing cryptography
scalability optimization
cloud
provable security
symbolic analysis

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Published Papers (12 papers)

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Research

20 pages, 3692 KiB

Open AccessArticle

A Privacy-Preserving and Quality-Aware User Selection Scheme for IoT

by Bing Han, Qiang Fu, Hongyu Su, Cheng Chi, Chuan Zhang and Jing Wang

Mathematics 2024, 12(19), 2961; https://doi.org/10.3390/math12192961 - 24 Sep 2024

Viewed by 585

Abstract

In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these mobile users is a key indicator in selecting high-quality participants, as it directly reflects the reliability of the data they submit and their past performance. However, existing approaches often rely on a trusted centralized server, which can lead to single points of failure and increased vulnerability to attacks. Additionally, they may not adequately address the potential manipulation of reputation scores by malicious entities, leading to unreliable and potentially compromised user selection. To address these challenges, we propose PRUS, a privacy-preserving and quality-aware user selection scheme for IoT. By leveraging the decentralized and immutable nature of the blockchain, PRUS enhances the reliability of the user selection process. The scheme utilizes a public-key cryptosystem with distributed decryption to protect the privacy of users’ data and reputation, while truth discovery techniques are employed to ensure the accuracy of the collected data. Furthermore, a privacy-preserving verification algorithm using reputation commitment is developed to safeguard against the malicious tampering of reputation scores. Finally, the Dirichlet distribution is used to predict future reputation values, further improving the robustness of the selection process. Security analysis demonstrates that PRUS effectively protects user privacy, and experimental results indicate that the scheme offers significant advantages in terms of communication and computational efficiency. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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21 pages, 1325 KiB

Open AccessArticle

EVFL: Towards Efficient Verifiable Federated Learning via Parameter Reuse and Adaptive Sparsification

by Jianping Wu, Chunming Wu, Chaochao Chen, Jiahe Jin and Chuan Zhou

Mathematics 2024, 12(16), 2479; https://doi.org/10.3390/math12162479 - 10 Aug 2024

Viewed by 723

Abstract

Federated learning (FL) demonstrates significant potential in Industrial Internet of Things (IIoT) settings, as it allows multiple institutions to jointly construct a shared learning model by exchanging model parameters or gradient updates without the need to transmit raw data. However, FL faces risks related to data poisoning and model poisoning. To address these issues, we propose an efficient verifiable federated learning (EVFL) method, which integrates adaptive gradient sparsification (AdaGS), Boneh–Lynn–Shacham (BLS) signatures, and fully homomorphic encryption (FHE). The combination of BLS signatures and the AdaGS algorithm is used to build a secure aggregation protocol. These protocols verify the integrity of parameters uploaded by industrial agents and the consistency of the server’s aggregation results. Simulation experiments demonstrate that the AdaGS algorithm significantly reduces verification overhead through parameter sparsification and reuse. Our proposed algorithm achieves better verification efficiency compared to existing solutions. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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18 pages, 391 KiB

Open AccessArticle

An Effective Federated Object Detection Framework with Dynamic Differential Privacy

by Baoping Wang, Duanyang Feng, Junyu Su and Shiyang Song

Mathematics 2024, 12(14), 2150; https://doi.org/10.3390/math12142150 - 9 Jul 2024

Viewed by 858

Abstract

The proliferation of data across multiple domains necessitates the adoption of machine learning models that respect user privacy and data security, particularly in sensitive scenarios like surveillance and medical imaging. Federated learning (FL) offers a promising solution by decentralizing the learning process, allowing multiple participants to collaboratively train a model without sharing their data. However, when applied to complex tasks such as object detection, standard FL frameworks can fall short in balancing the dual demands of high accuracy and stringent privacy. This paper introduces a sophisticated federated object detection framework that incorporates advanced differential privacy mechanisms to enhance privacy protection. Our framework is designed to work effectively across heterogeneous and potentially large-scale datasets, characteristic of real-world environments. It integrates a novel adaptive differential privacy model that strategically adjusts the noise scale during the training process based on the sensitivity of the features being learned and the progression of the model’s accuracy. We present a detailed methodology that includes a privacy budget management system, which optimally allocates and tracks privacy expenditure throughout training cycles. Additionally, our approach employs a hybrid model aggregation technique that not only ensures robust privacy guarantees but also mitigates the degradation of object detection performance typically associated with DP. The effectiveness of our framework is demonstrated through extensive experiments on multiple benchmark datasets, including COCO and PASCAL VOC. Our results show that our framework not only adheres to strict DP standards but also achieves near-state-of-the-art object detection performance, underscoring its practical applicability. For example, in some settings, our method can lower the privacy success rate by 40% while maintaining high model accuracy. This study makes significant strides in advancing the field of privacy-preserving machine learning, especially in applications where user privacy cannot be compromised. The proposed framework sets a new benchmark for implementing federated learning in complex, privacy-sensitive tasks and opens avenues for future research in secure, decentralized machine learning technologies. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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22 pages, 868 KiB

Open AccessArticle

Enhancing Real-Time Traffic Data Sharing: A Differential Privacy-Based Scheme with Spatial Correlation

by Junqing Le, Bowen Xing, Di Zhang and Dewen Qiao

Mathematics 2024, 12(11), 1722; https://doi.org/10.3390/math12111722 - 31 May 2024

Viewed by 756

Abstract

The real-time sharing of traffic data can offer improved services to users and timely respond to environmental changes. However, this data often involves individuals’ sensitive information, raising substantial privacy concerns. It is imperative to find ways to protect the privacy of the shared traffic data while maintaining its ongoing data utility. In this paper, a Differential Privacy-based scheme with Spatial Correlation for Real-time traffic data (named as DP-SCR) is proposed. DP-SCR not only ensures the high data utility of shared traffic data, but also provides strong privacy protection. Specifically, DP-SCR is designed to adhere to w-event

ε

-differential privacy, ensuring a high level of privacy protection. Subsequently, a novel adaptive allocation based on spatial correlation prediction is proposed to optimize the privacy budget allocation in differential privacy. In addition, a feasible dynamic clustering algorithm is developed to minimize the relative perturbation error, which further improves the quality of shared data. Finally, the analyses demonstrate that DP-SCR provides w-event privacy for the shared data of each section, and the spatial correlation is a more pronounced characteristic of the traffic data than other characteristics. Meanwhile, experiments conducted on real-world data show that the MAR and MER of the predicted data in DP-SCR are smaller than those in other baseline DP-based schemes. It indicates that the DP-SCR scheme proposed in this paper can provide more accurate shared data. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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17 pages, 778 KiB

Open AccessArticle

Vehicle Collaborative Partial Offloading Strategy in Vehicular Edge Computing

by Ruoyu Chen, Yanfang Fan, Shuang Yuan and Yanbo Hao

Mathematics 2024, 12(10), 1466; https://doi.org/10.3390/math12101466 - 9 May 2024

Cited by 2 | Viewed by 1179

Abstract

Vehicular Edge Computing (VEC) is a crucial application of Mobile Edge Computing (MEC) in vehicular networks. In VEC networks, the computation tasks of vehicle terminals (VTs) can be offloaded to nearby MEC servers, overcoming the limitations of VTs’ processing power and reducing latency caused by distant cloud communication. However, a mismatch between VTs’ demanding tasks and MEC servers’ limited resources can overload MEC servers, impacting Quality of Service (QoS) for computationally intensive tasks. Additionally, vehicle mobility can disrupt communication with static MEC servers, further affecting VTs’ QoS. To address these challenges, this paper proposes a vehicle collaborative partial computation offloading model. This model allows VTs to offload tasks to two types of service nodes: collaborative vehicles and MEC servers. Factors like a vehicle’s mobility, remaining battery power, and available computational power are also considered when evaluating its suitability for collaborative offloading. Furthermore, we design a deep reinforcement learning-based strategy for collaborative partial computation offloading that minimizes overall task delay while meeting individual latency constraints. Experimental results demonstrate that compared to traditional approaches without vehicle collaboration, this scheme significantly reduces latency and achieves a significant reduction (around 2%) in the failure rate under tighter latency constraints. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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18 pages, 720 KiB

Open AccessArticle

Mixture Differential Cryptanalysis on Round-Reduced SIMON32/64 Using Machine Learning

by Zehan Wu, Kexin Qiao, Zhaoyang Wang , Junjie Cheng and Liehuang Zhu

Mathematics 2024, 12(9), 1401; https://doi.org/10.3390/math12091401 - 3 May 2024

Viewed by 1210

Abstract

With the development of artificial intelligence (AI), deep learning is widely used in various industries. At CRYPTO 2019, researchers used deep learning to analyze the block cipher for the first time and constructed a differential neural network distinguisher to meet a certain accuracy. In this paper, a mixture differential neural network distinguisher using ResNet is proposed to further improve the accuracy by exploring the mixture differential properties. Experiments are conducted on SIMON32/64, and the accuracy of the 8-round mixture differential neural network distinguisher is improved from 74.7% to 92.3%, compared with that of the previous differential neural network distinguisher. The prediction accuracy of the differential neural network distinguisher is susceptible to the choice of the specified input differentials, whereas the mixture differential neural network distinguisher is less affected by the input difference and has greater robustness. Furthermore, by combining the probabilistic expansion of rounds and the neutral bit, the obtained mixture differential neural network distinguisher is extended to 11 rounds, which can realize the 12-round actual key recovery attack on SIMON32/64. With an appropriate increase in the time complexity and data complexity, the key recovery accuracy of the mixture differential neural network distinguisher can be improved to 55% as compared to 52% of the differential neural network distinguisher. The mixture differential neural network distinguisher proposed in this paper can also be applied to other lightweight block ciphers. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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19 pages, 1394 KiB

Open AccessArticle

A Blockchain-Based Secure Sharing Scheme for Electrical Impedance Tomography Data

by Ruwen Zhao, Chuanpei Xu, Zhibin Zhu and Wei Mo

Mathematics 2024, 12(7), 1120; https://doi.org/10.3390/math12071120 - 8 Apr 2024

Cited by 1 | Viewed by 1192

Abstract

Real-time electrical impedance tomography (EIT) data sharing is becoming increasingly necessary, due to the extensive use of EIT technology in various sectors, including material analysis, biomedicine, and industrial process monitoring. The prevalence of portable EIT equipment and remote imaging technology has led to a predominance of centralized storage, Internet protocol transmission, and certificates from certificate authorities (CA) in telemedicine data. This has resulted in compromised data security, network communication delays, high CA maintenance costs, increased risks of medical data privacy breaches, and low security. Therefore, this paper offers a consortia blockchain-based method for exchanging EIT data that addresses security and integrity concerns during data storage and exchange, while maintaining transparency and traceability. Proprietary re-encryption techniques are employed to guarantee traceability when exchanging anonymous data, enabling precise control over data access. This scheme serves to protect both data and identity privacy, as well as to trace the actual identity of potential malicious users, while also thwarting any coordinated efforts between partially trusted parties and data requesters seeking unauthorized access to confidential information. Additionally, a combination of blockchain and InterPlanetary File System (IPFS) distributed storage technology is utilized to ease the burden of EIT data storage. The feasibility and effectiveness of the proposed solution were validated through a series of experiments, demonstrating its ability to effectively prevent data tampering and misuse, reduce data management costs, and enhance the efficiency and quality of data sharing. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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19 pages, 914 KiB

Open AccessArticle

A Privacy-Preserving Multilingual Comparable Corpus Construction Method in Internet of Things

by Yu Weng, Shumin Dong and Chaomurilige

Mathematics 2024, 12(4), 598; https://doi.org/10.3390/math12040598 - 17 Feb 2024

Viewed by 1085

Abstract

With the expansion of the Internet of Things (IoT) and artificial intelligence (AI) technologies, multilingual scenarios are gradually increasing, and applications based on multilingual resources are also on the rise. In this process, apart from the need for the construction of multilingual resources, privacy protection issues like data privacy leakage are increasingly highlighted. Comparable corpus is important in multilingual language information processing in IoT. However, the multilingual comparable corpus concerning privacy preserving is rare, so there is an urgent need to construct a multilingual corpus resource. This paper proposes a method for constructing a privacy-preserving multilingual comparable corpus, taking Chinese–Uighur–Tibetan IoT based news as an example, and mapping the different language texts to a unified language vector space to avoid sensitive information, then calculates the similarity between different language texts and serves as a comparability index to construct comparable relations. Through the decision-making mechanism of minimizing the impossibility, it can identify a comparable corpus pair of multilingual texts based on chapter size to realize the construction of a privacy-preserving Chinese–Uighur–Tibetan comparable corpus (CUTCC). Evaluation experiments demonstrate the effectiveness of our proposed provable method, which outperforms in accuracy rate by 77%, recall rate by 34% and F value by 47.17%. The CUTCC provides valuable privacy-preserving data resources support and language service for multilingual situations in IoT. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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15 pages, 4933 KiB

Open AccessArticle

RHCA: Robust HCA via Consistent Revoting

by Zijian Zhang, Kaiyu Feng, Xi Chen, Xuyang Liu and Haibo Sun

Mathematics 2024, 12(4), 593; https://doi.org/10.3390/math12040593 - 17 Feb 2024

Cited by 2 | Viewed by 856

Abstract

Since the emergence of blockchain, how to improve its transaction throughput and reduce transaction latency has always been an important issue. Hostuff has introduced a pipeline mechanism and combined it with a chain structure to improve the performance of blockchain networks. HCA has introduced a revoting mechanism on the basis of Hostuff, further reducing transaction latency, but it has also brought some problems. In HCA, if the leader is malicious, it would be possible to continuously call on the replica nodes to revote, which can lead to network congestion. This paper employs the global perfect coin technology to guarantee that every replica can obtain a globally consistent and the freshest candidate proposal during the Revote phase, thereby improving the robustness of the HCA protocol. The performance improvement of RHCA in attack scenarios has been verified through experiments. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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24 pages, 980 KiB

Open AccessArticle

A Blockchain-Enabled Group Covert Channel against Transaction Forgery

by Tongzhou Shen, Liehuang Zhu, Feng Gao, Zhuo Chen, Zijian Zhang and Meng Li

Mathematics 2024, 12(2), 251; https://doi.org/10.3390/math12020251 - 12 Jan 2024

Cited by 2 | Viewed by 1172

Abstract

As a decentralized network infrastructure, the data sent to the blockchain are public and temper-evident. The cover of massive normal transactions in a blockchain network is ideal for constructing a stable and reliable covert channel to achieve one-to-many group covert communication. Existing blockchain-based covert communication schemes face challenges in balancing concealment, embedding rate and filtering efficiency, making them unsuitable for direct extension to group scenarios. Adopting a key-leakage scheme can increase the channel capacity while maintaining high concealment from external adversaries. However, it will also expose more knowledge to the receiver. A malicious receiver has the ability to steal a sender’s identity or replay historical transactions to control the entire channel. In this paper, we define the capabilities of malicious receivers in blockchain-based group covert communication scenarios and propose a group covert communication scheme resistant to transaction forgery attacks. Theoretical analysis and experiments prove that our covert transactions do not have any transaction correlativity, ensuring the unique authenticity of the sender’s identity while maintaining supreme concealment compared with the existing schemes. The precision and recall of machine learning detection results can reach 0.57–0.62 (0.5 is the ideal value). Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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21 pages, 762 KiB

Open AccessArticle

SmartMeasurer: A Secure and Automated Bandwidth Measurement for Tor with Smart Contract

by Zejia Tang, Tianyao Pan, Yang Han, Tongzhou Shen, Lei Xu and Dawei Xu

Mathematics 2023, 11(19), 4105; https://doi.org/10.3390/math11194105 - 28 Sep 2023

Cited by 1 | Viewed by 1256

Abstract

Tor is now using a centralized measurement system called Sbws to measure the bandwidth of relays to guide clients in selecting relays to balance traffic. Sbws has been proven to be vulnerable to multiple attacks because of the centralized structure and exposed measurements. We present SmartMeasurer, a secure and decentralized system for bandwidth measurement. Combining smart contract, Oracle Chainlink and ECC technology, we achieve decentralization while hiding the measurement circuits among the general circuits by exploiting the dual identity of randomly dynamic measurers and guards. We analyze the security of our system and demonstrate that it defends against three types of attacks. Our experiments on both private and public Tor networks show that our system is decentralized while keeping the error and the average of our measurements converges to a small interval of 0.30 Mbps. Compared to other existing methods, our system reduces trust assumptions and the costs of using smart contract, and enhances the practical feasibility of the solution. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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19 pages, 784 KiB

Open AccessArticle

A Practical Website Fingerprinting Attack via CNN-Based Transfer Learning

by Tianyao Pan, Zejia Tang and Dawei Xu

Mathematics 2023, 11(19), 4078; https://doi.org/10.3390/math11194078 - 26 Sep 2023

Cited by 1 | Viewed by 1352

Abstract

Website fingerprinting attacks attempt to apply deep learning technology to identify websites corresponding to encrypted traffic data. Unfortunately, to the best of our knowledge, once the total number of encrypted traffic data becomes insufficient, the identification accuracy in most existing works will drop dramatically. This phenomenon grows worse because the statistical features of the encrypted traffic data are not always stable but irregularly varying in different time periods. Even a deep learning model requires good performance to capture the statistical features, its accuracy usually diminishes in a short period of time because the changes of the statistical features technically put the training and testing data into two non-identical distributions. In this paper, we first propose a convolutional neural network-based website fingerprinting attack (CWFA) scheme. This scheme integrates packet direction with the timing sequence from the encrypted traffic data to improve the accuracy of analysis as much as possible on few data samples. We then design a new fine-tuning mechanism for the CWFA (FM-CWFA) scheme based on transfer learning. This mechanism enables the proposed FM-CWFA scheme to support the changes in the statistical patterns. The experimental results in closed-world and open-world settings show that the effectiveness of the CWFA scheme is better than previous researches, with the slowest performance degradation when the number of data decreases, and the FM-CWFA scheme can remain effective when the statistical features change. Full article

(This article belongs to the Special Issue Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis)

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