Data integrity, a core security issue in reliable cloud storage, has received much attention. Data auditing protocols enable a verifier to efficiently check the integrity of the outsourced data without downloading the data. A key research challenge associated with existing designs of data auditing protocols is the complexity in key management. In this paper, we seek to address the complex key management challenge in cloud data integrity checking by introducing fuzzy identity-based auditing, the first in such an approach, to the best of our knowledge. More specifically, we present the primitive of fuzzy identity-based data auditing, where a user's identity can be viewed as a set of descriptive attributes. We formalize the system model and the security model for this new primitive. We then present a concrete construction of fuzzy identity-based auditing protocol by utilizing biometrics as the fuzzy identity. The new protocol offers the property of error-tolerance, namely, it binds with private key to one identity which can be used to verify the correctness of a response generated with another identity, if and only if both identities are sufficiently close. We prove the security of our protocol based on the computational Diffie-Hellman assumption and the discrete logarithm assumption in the selective-ID security model. Finally, we develop a prototype implementation of the protocol which demonstrates the practicality of the proposal.

Monero provides a high level of anonymity for both users and their transactions. However, many criminal activities might be committed with the protection of anonymity in cryptocurrency transactions. Thus, user accountability (or traceability) is also important in Monero transactions, which is unfortunately lacking in the current literature. In this paper, we fill this gap by introducing a new cryptocurrency named \textit{Traceable Monero} to balance the user anonymity and accountability. Our framework relies on a tracing authority, but is optimistic, in that it is only involved when investigations in certain transactions are required. We formalize the system model and security model of Traceable Monero. We present a detailed construction of Traceable Monero by overlaying Monero with two types of tracing mechanisms, tracing the one-time addresses with money flows and tracing the long-term addresses. We prove the security of Traceable Monero and implement a prototype of the system, which demonstrates that Traceable Monero incurs merely a very small overhead in generating and verifying a transaction compared to Monero transactions.

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. However, most of the existing IoT infrastructures are centralized, which may cause the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology called blockchain. Voting systems are widely adopted in IoT, for example a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to unsuitable, traditional centralized voting systems in decentralized IoT. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused by malicious voters. To address these issues, in this paper, we introduce a framework of the self-tallying voting system in decentralized IoT based on blockchain. We propose a concrete construction and prove that the proposed system satisfies all the security requirements, including fairness, dispute-freeness and maximal ballot secrecy. We simulate the algorithms on a laptop, an Android phone and a Raspberry Pi to test the time consumption and evaluate the gas cost of each algorithm in a private blockchain as well. The implementation results demonstrate the practicability of our system

Personal privacy disclosure is one of the most serious challenges in smart industrial applications. Anonymous authentication is an effective solution to protect personal privacy. However, the existing anonymous credential protocols are not perfectly suitable for smart industrial environments such as smart vehicles in the sense that the credential revocation issue is not well solved. In this paper, we propose BASS, a Blockchain-based Anonymous authentication with Selective revocation for Smart industrial applications supporting attribute privacy, selective revocation, credential soundness and multi-showing-unlinkability. According to the diverse demands of credential authorities, BASS can selectively provide revocation of credentials or revocation of users. We extend BASS from single-attribute privacy to multi-attribute privacy as well. Finally, we implement a prototype to evaluate the cryptographic core primitives of BASS by deploying smart contracts in Ethereum to demonstrate the validity of BASS in smart industrial applications.

The advances of cloud computing, fog computing and Internet of Things (IoT) make industries more prosperous than ever. However, in this sophisticated system security and privacy issues are major concerns. In this paper, we focus on the issue of assured data deletion. We firstly propose a framework to integrate the cloud, the fog and the things together to manage stored data from industries or individuals. We then focus on secure data deletion in this framework by proposing an assured data deletion scheme which fulfills verifiable data deletion as well as flexible access control over sensitive data. Only data owners and fog devices are involved when deleting cloud data and validating the deletion of these data, which makes the protocol practical. The proposed protocol takes advantage of attribute-based encryption, whose security can be proved under standard model. The theoretical analysis and the implementation results demonstrate the feasibility of our proposal.