Machine learning models based on artificial neural networks have been widely adopted to support diverse complex applications. However, the training of such models heavily relies on large-scale datasets, which may raise privacy concerns. Taking the Internet of Things (IoT) as an example, distributed edge devices collect data, while central servers aggregate this data for model training and in-depth analysis. Unlike frameworks like federated learning, local model training becomes infeasible due to constrained edge device capabilities, model confidentiality requirements, or the separation of complete data features across multiple devices. This scenario may render traditional federated learning-based privacy-preserving frameworks ineffective. To address these limitations, we propose an efficient privacy-preserving model training protocol that demonstrates practical advantages over fully homomorphic encryption and functional encryption methods. Our protocol leverages additively homomorphic encryption combined with the Chinese Remainder Theorem to design secure matrix-vector computations, minimizing encryption/decryption operations and reducing computational overhead on edge devices while preserving training efficiency and model accuracy. To further optimize performance, we further propose a secondary protocol that introduces optimization strategies to enhance efficiency and lighten edge nodes’ computational burdens. Security analysis and rigorous correctness proofs are provided, and performance evaluations and experimental validation confirm both protocols’ practical feasibility and effectiveness.

Among various blockchain applications, decentralized anonymous payment (DAP) systems stand out for their enhanced privacy protection compared to traditional payment methods. However, DAPs face challenges such as the lack of asset recovery and identity verification features. To ensure the long-term healthy development of DAP systems, adherence to legal regulations and privacy protection is equally critical. In response to these requirements, we propose a$\textsf {CR}$-$\textsf {DAP}$system that offers a secure and efficient solution without compromising on practicality. Our innovation lies in introducing an identity-based traceable anonymous signature scheme, which skillfully balances anonymity with traceability. This scheme supports private key retrieval and allows for identity tracking when necessary, addressing key pain points in existing anonymous payment systems and enhancing user trust. We have implemented the prototype of this signature scheme and the$\textsf {CR}$-$\textsf {DAP}$system, evaluating its performance to demonstrate its practicality.

While blockchain is known to support open and transparent data exchange, partly due to its nontamperability property, it can also be (ab)used to facilitate the spreading of fake and misleading information or information that was subsequently discredited. Hence, this paper proposes a practical, redactable blockchain framework with a public trapdoor (hereafter referred to as PRBFPT). PRBFPT comprises an editing scheme for adding blocks using a new type of blockchain with a chameleon hash. Specifically, PRBFPT is able to involve all nodes in the blockchain in the editing operations by means of a public trapdoor, without requiring additional trapdoor management by predefined nodes or organizations. PRBFPT is also designed to audit and record the content of each editing operation. In other words, after editing and deleting the original data, PRBFPT can still verify its legitimacy. We also propose a contract-based locked voting scheme to better support voting. We then evaluate the prototype implementation of PRBFPT, whose findings show that the total time consumption of adding modules is at the millisecond level, with a negligible impact on the performance of the original system. In addition, the evaluation findings show that the cost of initiating the special transactions is comparable to the consumption of normal Ethereum transactions and is within a manageable range.

In our data-centric society, major service providers have access to vast amounts of user information (e.g., user-generated content such as social media posts, and device-generated content such as geolocation data) for convenient and efficient services. There are privacy implications when users authorize share personal data managed by service providers. To make authorization private and controllable, in this paper, we propose a private authorization scheme oriented service providers. A decentralized publicly-verifiable re-encryption method based on IPFS is proposed to minimize the reliance on service providers, by shifting to a distributed storage and computation model. Besides, we propose a trustless authorization authentication method that hides the authorization relationship to protect user privacy. We also evaluate the security of our scheme, as well as its performance to demonstrate utility.

Blockchain technology has gained prominence for its potential to address security and privacy challenges in Internet-of-Things (IoT) services and Cyber-Physical Systems (CPS) due to its decentralized, traceable, and immutable nature. However, the considerable energy consumption associated with blockchain, exemplified by Bitcoin, has raised sustainability concerns. This paper introduces BitFT, a consensus protocol that combines the strengths of both lottery-based and voting-based mechanisms to offer a sustainable, comprehensible, and high-performance solution. BitFT dissects the block lifecycle into three phases: dissemination, and commitment phases, which correspond to the Bitcoin framework. It leverages a multiple-round sortition algorithm, a Reliable Broadcast (Rbc) protocol, and a Quorum Certificate (QC) mechanism to facilitate efficient protocol operation. The sortition algorithm functions like a lottery algorithm, while theRbcprotocol andQCmechanism are implemented based on votes. In order to maximize network utilization and enhance system throughput, we further introduce a layered architecture to BitFT, which allows for concurrent commitment of multiple blocks at the same height. We perform a comprehensive analysis to verify the correctness of BitFT and conduct various experiments to demonstrate its high performance.

Medical data sharing can improve the quality of medical care and promote progress in the field of public health. However, medical data has high confidentiality and complexity. Taking the clinical diagnosis and treatment data of Diabetes Mellitus Type 2 (T2DM) in medical treatment as an example, T2DM data often has the phenomenon of "multi-disease coexistence" and is insecure and inaccurate in the process of sharing. Based on Blockchain and Attribute- Encryption (ABE), we propose a T2DM data sharing system to solve the above problems. An Attribute-Encryption algorithm T2DM-CP-ABE is proposed to solve the problem of excessive sharing granularity. T2DM-CP-ABE, combined with the medical data contribution module, takes the contribution factor as an important consideration, to stimulate the power of T2DM data sharing and promote the development of national diabetes prevention. Based on the Blockchain, a medical data attribute authorization model is proposed, which provides a credible solution for multiple hospitals to realize medical data attribute authorization, and realizes the clinical diagnosis and treatment data sharing of T2DM among doctors. Based on Blockchain and ABE technology, a data sharing system for T2DM has been implemented on the Fabric platform. The result shows that the average time for the system to complete a single sharing is less than 1 second.

The phenomenon of unbalanced regional medical resources has led to large differences in the implementation experience of clinical pathways in hospitals with different medical levels. However, due to the fear of privacy leakage and the lack of sharing motivation, there is a lack of effective collaborative communication channels for clinical pathways among medical institutions. In response to these problems, we propose a blockchain-based sharing incentive scheme for clinical pathway experience, which uses the clinical pathway implementation effect evaluation system to evaluate the quality of clinical pathway experience data. Specifically, we designe a two-phase cross-domain shared transaction model for the transaction of clinical pathway experience data. Moreover, we introduce the shared transaction alliance committee to verify and review the transaction, and solve the problems in the transaction in the arbitration phase. Finally, the functional test results show that the system meets the experience sharing incentive requirements, and the performance test results show that the TPS of sharing clinical pathway experience read and writed can reach around 600 and 1000, and the transaction latency of each phase is within 3 s.

Data is a key asset in our interconnected and smart city. Especially, in the context of healthcare, healthcare data can facilitate remote diagnosis and medical research. Because of the potentially sensitive nature of healthcare data, privacy is a key consideration for both individuals and organizations. We can broadly categorize privacy considerations into data privacy, attribute privacy, and privilege policy privacy. To support one or more notions of privacy, the potential of solutions, such as fine-grained access control [e.g., those based on attribute-based encryption (ABE)] and blockchain in realizing data sharing has been explored. However, these approaches generally only facilitate access control of data and the traceability of the sharing process, and do not protect the attribute and privilege policy privacy of users. Therefore, in this article, we implement HAPPS, a hidden attribute and privilege-protection data-sharing scheme with verifiability. The three key building blocks of HAPPS are zero-knowledge proof, blockchain, and distributed ABE (DABE). Specifically, in our approach, we propose a new data access control strategy (i.e., attribute-hidden zero-knowledge proof—at-ZKP) to hide user identity and attributes during the authorization process. Our scheme is embedded in the blockchain and built into the decentralized sharing platform to prevent central verifier counterfeiting and support auditing. To demonstrate utility, we prove that HAPPS ensures data, attribute, and privilege policy privacy. Findings of our evaluations implemented on Ethereum and using the data set from the healthcare cost and utilization project (HCUP), we demonstrate that our scheme can share sensitive healthcare records belonging to minors (e.g., children) without the at-ZKP incurring unrealistic cost.

Ongoing smart contract attack events have seriously impeded the practical application of blockchain. Although lots of researches have been conducted, they mostly focus on off-chain vulnerability detection. However, smart contracts cannot be modified once they have been deployed on-chain, thus existing techniques cannot protect those deployed contracts from being attacked. To mitigate this problem, we propose a general smart contract repairer named Aroc, which can automatically patch vulnerable deployed contracts without changing the contract codes. The core insight of Aroc is to generate patch contracts to abort malicious transactions in advance. Taking the three most serious bug types (i.e., reentrancy, arithmetic bugs, and unchecked low-level checks) as examples, we present how Aroc automatically repairs them on-chain. We conduct abundant evaluations on four kinds of datasets to evaluate the effectiveness and efficiency of Aroc. In particular, Aroc can repair 95.95% of the vulnerable contracts with an average correctness ratio of 93.32%. Meanwhile, Aroc introduces acceptable additional overheads to smart contract users and blockchain miners. When compared with the state-of-the-art techniques, Aroc introduces either fewer execution overheads or contract codes.

As cryptocurrency and blockchain-related assets become more common in our digital society, there is a corresponding need to secure our digital assets, including the private keys used to secure access to such assets (e.g., due to loss or corruption of the data storage medium). However, there are limitations in existing blockchain-related asset management and recovery methods. Therefore, we use zero-knowledge proof to design a cryptocurrency recovery scheme based on hidden assisting relationships (hereafter referred to as the CRSA scheme) to facilitate the recovery of blockchain assets. Specifically, when the user's private key is lost, and access to the assets cannot be obtained, the user leverages information such as the pre-defined list of assistants to authenticate himself/herself on the blockchain. Once the assistants have confirmed the legitimacy of the user's authentication request, the asset will be transferred from the old address to the new address. During the (identity) proof process, the zero-knowledge proof is used to ensure that the identification of assistants is not leaked to other nodes, assistants, and the adversary. We provide the formal definition of the above scheme and the security proof of the construction. We also implement a prototype of the system and evaluate its performance. Evaluations indicate that the time required for the zero-knowledge proof is less than 10s, and the block verification time is less than 100ms.

Data, a key asset in our data-driven economy, has fueled the emergence of a new data trading industry. However, there are a number of limitations in conventional data trading platforms due to the existence of dishonest buyer/data broker. To mitigate these limitations, we posit the importance of a data processing-as-a-service model, which complements the conventional data hosting/exchange-as-a-service model. Specifically, in this paper, we introduce a secure data trading ecosystem and present a new blockchain-based data trading ecosystem (hereafter referred to as SDTE). In the ecosystem, both data broker and buyer are not able to obtain access to the seller's raw data, as they are only getting access to the analysis findings that they require. In other words, we reduce the challenge of securing the dataset to the challenge to secure the data processing. We also build a security model to analyze the data trading market and describe a new set of trading protocols for the entire data trading market. To demonstrate utility, we implement our proposed secure data trading platform (SDTP) on Ethereum & Intel's Software Guard Extensions (SGX) and perform an in-depth analysis.

Research has shown that smart contracts have become a significant and promising technology. However, the lack of mature and stable security mechanism, such as access control, makes smart contracts quite vulnerable. To mitigate this problem, we present an attributed-based access control system based on blockchain by building a hierarchical management mechanism of contract administrator and contract owner. After combining blockchain inherent synchronization function with our designed system-level smart contract, all peers can easily get access control rules timely. Moreover, we modify the original smart contract finite state machine to prevent malicious modification on the rules through system contract. In our evaluation, the system only introduces 2%-5% additional cost with assured security on Hyperledger.

In the paradigm of infrastructure-as-a-service cloud computing involving an Internet of Things network, customers outsource their infrastructure to the cloud. An outsourced infrastructure is a virtual infrastructure that mimics the physical infrastructure of the precloud era; it is therefore referred to as a tenant network (TN) in this paper. This practice draws upon the notion of TN abstraction, which specifies how TNs should be managed. However, current virtual software-defined network (SDN) technology uses an SDN hypervisor to attain TNs, where the cloud administrator is given much-more-than-necessary privileges; thus, not only could violation of the security principle of least privilege occur, but the threat of a malicious or innocent-but-compromised administrator may be present. Motivated by this need, we propose the specification of TN abstraction, including its functions and security requirements. Then, we present a platform-independent concretization of this abstraction called TNGuard, which is an SDN-based architecture that protects the TNs while removing unnecessary privileges from the cloud administrator. In order to show that TNGuard concretizes the TN abstraction, we present an instantiation of TNGuard on the Xen virtualization platform with the Ryu controller. Experimental results show that the resulting system is practical, incurring a small performance overhead.

Summary Cryptographic functions, such as encryption/decryption libraries, are common and important tools for applications to enhance confidentiality of the data. However, these functions could be compromised by subtle attacks launched by untrusted operating system or other applications, and sensitive keys or cryptographic procedures could then be compromised. In this paper, based on virtualization technology, we propose a novel approach that outsources the cryptographic functions in one virtual machine (VM) into another dedicated VM, so that sensitive keys and the cryptographic procedures are only contained by this VM with specific purpose. We also propose a prototype, called cryptographic function assurance (CFA), to enhance the security of cryptographic functions. Taking OpenSSL as an example, CFA allows those applications that use OpenSSL library to transparently utilize CFA to protect the cryptographic functions. We present the detailed implementation, as well as the security analysis of CFA. We also give the performance evaluation for OpenSSL's interfaces and Apache httpd, to show the overhead caused by the integration of CFA. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract With the widespread use of smartphones, the loss of a device is a critical problem, which results both in disrupting daily communications and losing valuable property. As a result, tracking systems have been developed to track mobile devices. Previous tracking systems focus on recovering the device's locations after it goes missing, with security methods implemented on the clients. However, users' locations are stored in untrusted third‐party services, which may be attacked or eavesdropped. In this paper, we propose a system, named Android Cloud Tracker, to provide a privacy‐preserving tracking client and safe storing of user's locations. We use cloud storage controlled by users themselves as storage facilities, and they do not need to worry about any untrusted third party. We implement Android Cloud Tracker prototype on Android phones, and the evaluation shows that it is both practical and lightweight: it generates a small amount of data flow and its distributed architecture provides strong guarantees of location privacy while preserving the ability to efficiently track missing devices. Copyright © 2014 John Wiley & Sons, Ltd.

Due to the rapid development of cloud computing, several cloud computing platforms are developed to build cloud for individuals and companies. Open Nebula is known as one of the most popular open-source cloud computing software platforms. However, Open Nebula does not perform effectively in security. Virtual machines face risks of being attacked, which leads to services halt and data loss. We analyze the latest version of Open Nebula as well as some other similar products, and find out some functions are missing in these software platforms which turn out to be essential in secure cloud environment. We present ON Help, security components assisting Open Nebula to build a securer cloud platform, including trustworthiness attestation of computing nodes and VMs, deep monitoring of VMs, service-level fault tolerant service, cloud anti-virus in VMs and secure cloud storage. Our experimental results show that ON Help can be easily deployed with Open Nebula when constructing a cloud computing platform, and it can enhance the stability of cloud service and the security of cloud computing environment.

Digital signatures are an important mechanism for ensuring data trustworthiness via source authenticity, integrity, and source nonrepudiation. However, their trustworthiness guarantee can be subverted in the real world by sophisticated attacks, which can obtain cryptographically legitimate digital signatures without actually compromising the private signing key. This problem cannot be adequately addressed by a purely cryptographic approach, by the revocation mechanism of Public Key Infrastructure (PKI) because it may take a long time to detect the compromise, or by using tamper-resistant hardware because the attacker does not need to compromise the hardware. This problem will become increasingly more important and evident because of stealthy malware (or Advanced Persistent Threats). In this paper, we propose a novel solution, dubbed Assured Digital Signing (ADS), to enhancing the data trustworthiness vouched by digital signatures. In order to minimize the modifications to the Trusted Computing Base (TCB), ADS simultaneously takes advantage of trusted computing and virtualization technologies. Specifically, ADS allows a signature verifier to examine not only a signature's cryptographic validity but also its system security validity that the private signing key and the signing function are secure, despite the powerful attack that the signing application program and the general-purpose Operating System (OS) kernel are malicious. The modular design of ADS makes it application-transparent (i.e., no need to modify the application source code in order to deploy it) and almost hypervisor-independent (i.e., it can be implemented with any Type I hypervisor). To demonstrate the feasibility of ADS, we report the implementation and analysis of an Xen-based ADS system.

Cloud computing is believed to be the next major paradigm of computing because it will substantially reduce the cost of IT systems. Ensuring security in the cloud-end is necessary because customers' data are stored and processed there. Previous studies have mainly focused on secure cloud-end storage, whereas secure cloud-end computing is much less investigated. The current practice is solely based on Virtual Machines (VM), and cannot offer adequate security because the guest Operating Systems (OS) often can be easily breached (e.g., by exploiting their vulnerabilities). This motivates the need of solutions for more secure cloud-end computing. This poster presents the design, implementation and analysis of a candidate solution, called Trusted Execution Environment (TEE), which takes advantage of both virtualization and trusted computing technologies simultaneously. The novelty behind TEE is the virtualization of the Dynamic Root of Trust for Measurement (DRTM).