Current research on Internet of Things (IoT) security: A survey

This paper presents a comprehensive survey of the existing blockchain protocols for the Internet of Things (IoT) networks. We start by describing the blockchains and summarizing the existing surveys that deal with blockchain technologies. Then, we provide an overview of the application domains of blockchain technologies in IoT, e.g., Internet of Vehicles, Internet of Energy, Internet of Cloud, Edge computing, etc. Moreover, we provide a classification of threat models, which are considered by blockchain protocols in IoT networks, into five main categories, namely identity-based attacks, manipulation-based attacks, cryptanalytic attacks, reputation-based attacks, and service-based attacks. In addition, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving blockchain technologies with respect to the blockchain model, specific security goals, performance, limitations, computation complexity, and communication overhead. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the blockchain technologies for IoT.

Blockchain Technologies for the Internet of Things: Research Issues and Challenges

Controllable and trustworthy blockchain-based cloud data management

Security, privacy & efficiency of sustainable Cloud Computing for Big Data & IoT

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5216&context=sis_research

Blockchain based efficient and robust fair payment for outsourcing services in cloud computing

Identity-based cryptosystems mean that public keys can be directly derived from user identifiers, such as telephone numbers, email addresses, and social insurance number, and so on. So they can simplify key management procedures of certificate-based public key infrastructures and can be used to realize authentication in blockchain. Linearly homomorphic signature schemes allow to perform linear computations on authenticated data. And the correctness of the computation can be publicly verified. Although a series of homomorphic signature schemes have been designed recently, there are few homomorphic signature schemes designed in identity-based cryptography. In this paper, we construct a new ID-based linear homomorphic signature scheme, which avoids the shortcomings of the use of public-key certificates. The scheme is proved secure against existential forgery on adaptively chosen message and ID attack under the random oracle model. The ID-based linearly homomorphic signature schemes can be applied in e-business and cloud computing. Finally, we show how to apply it to realize authentication in blockchain.

An ID-Based Linearly Homomorphic Signature Scheme and Its Application in Blockchain

As a method for increasing throughput and improving reliability of routing, network coding has been widely used in decentralized IoT systems. When files are shared in the system, network coding signature techniques can help authenticate whether a modified packet in files is injected or not. However, in an IoT system, there are often multiple source devices each of which has its own authentication key, where existing single-source network coding signature schemes cannot work. In this paper, we study the problem of designing secure network coding signatures in the network with multiple sources and propose the multisource homomorphic network coding signature. We also give construction and prove its security.

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A Homomorphic Network Coding Signature Scheme for Multiple Sources and its Application in IoT

To gurantee the security and privacy of the patient’s physiological data in wirelss body area networks (WBANs), it is important to secure the communication between the personal digital assistance held by the WBANs client and the application provider, such as a medical institution, physician, or hospital. These physiological data are so large, traditional methods cannot process them efficiently and securely, thus big data services are needed. In the existing anonymous authentication schemes for WBANs, most of them did not consider when a malicious WBANs client sends a false message to cheat the application provider and cause a medical accident, how to trace the real identity of this client and punish him. In order to overcome the above issues, an efficient and certificateless conditional privacy-preserving authentication scheme for WBANs big data services is proposed in this paper. Due to the proposed scheme is based on big data, the capabilities of the proposed WBANs system is better than traditional WBANs. To improve the performance, the proposed scheme supports batch authentication of multiple clients, which significantly reduces the computational overhead of the application provider. Moreover, the proposed scheme provides anonymity, un-linkability, mutual authentication, traceability, session key establishment, forward secrecy, and attack resistance. The simulation experiment demonstrates that the proposed scheme for WBANs needs less computational time than recent schemes.

An Efficient and Certificateless Conditional Privacy-Preserving Authentication Scheme for Wireless Body Area Networks Big Data Services

Similarity-based integrity protection for deep learning systems

Differentially Private Stochastic Gradient Descent (DP‐SGD) is a prime method for training machine learning models with rigorous privacy guarantees. Since its birth, DP‐SGD has gained popularity and has been widely adopted in both academic and industrial research. One well‐known challenge when using DP‐SGD is how to improve utility while maintaining privacy. To this end, recently we have seen several proposals that clip the gradients with adaptive thresholds rather than a fixed one. Although each proposal comes with some theoretical justification, the theories often rely on strong assumptions and are not compatible with each other. It is hard to know whether they are good in practice and how good they are. In this paper, we investigate adaptive clipping in DP‐SGD from an empirical perspective. With extensive experiments, we were able to gain some fresh insights and proposed two new adaptive clipping strategies based on them. We cross‐compared the existing methods and our new strategies experimentally. Results showed that our strategies did provide a substantial improvement in model accuracy, and outperformed the state‐of‐the‐art adaptive clipping methods consistently.

Hongyang Yan

Papers

An ID-Based Linearly Homomorphic Signature Scheme and Its Application in Blockchain

A Homomorphic Network Coding Signature Scheme for Multiple Sources and its Application in IoT

An Efficient and Certificateless Conditional Privacy-Preserving Authentication Scheme for Wireless Body Area Networks Big Data Services

Similarity-based integrity protection for deep learning systems

Understanding adaptive gradient clipping in DP‐SGD, empirically