Blockchain is a technology that was proposed to enable the decentralized digital currency, Bitcoin. Since its inception, blockchain has been widely used in many other areas, including tracing sensor data and mitigating its duplication in IoT applications, the healthcare industry, and e-voting. In this paper, we provide a comprehensive review and analysis of the major security and privacy issues of Bitcoin and blockchain, the major challenges, and opportunities in utilizing the technology. First, we present a comprehensive background of Bitcoin and the preliminary on security. Second, the major security threats and countermeasures of Bitcoin are investigated. We analyze the risk of double-spending attacks, evaluate the probability of success in performing the attacks, and derive the profitability for the attacker to perform such attacks. Third, we analyze the underlying Bitcoin peer-to-peer network security risks and Bitcoin storage security. We compare three types of Bitcoin wallets in terms of security, types of services, and their trade-offs. Finally, we discuss the security and privacy features of alternative cryptocurrencies and present an overview of emerging technologies today. Our results can help Bitcoin users to determine a trade-off between the risk of double-spending attempts and the transaction time delay or confidence before accepting transactions. These results can also assist miners to develop suitable strategies to get involved in the mining process and maximize their profits.

The proliferation of online biometric authentication has necessitated security requirements of biometric templates. The existing secure biometric authentication schemes feature a server-centric model, where a service provider maintains a biometric database and is fully responsible for the security of the templates. The end-users have to fully trust the server in storing, processing and managing their private templates. As a result, the end-users’ templates could be compromised by outside attackers or even the service provider itself. In this paper, we propose a user-centric biometric authentication scheme (PassBio) that enables end-users to encrypt their own templates with our proposed light-weighted encryption scheme. During authentication, all the templates remain encrypted such that the server will never see them directly. However, the server is able to determine whether the distance of two encrypted templates is within a pre-defined threshold. Our security analysis shows that no critical information of the templates can be revealed under both passive and active attacks. PassBio follows a “compute-then-compare” computational model over encrypted data. More specifically, our proposed Threshold Predicate Encryption (TPE) scheme can encrypt two vectors x and y in such a manner that the inner product of x and y can be evaluated and compared to a pre-defined threshold. TPE guarantees that only the comparison result is revealed and no key information about x and y can be learned. Furthermore, we show that TPE can be utilized as a flexible building block to evaluate different distance metrics such as Hamming distance and Euclidean distance over encrypted data. Such a compute-then-compare computational model, enabled by TPE, can be widely applied in many interesting applications such as searching over encrypted data while ensuring data security and privacy.

Electronic voting (e-voting) presents a convenient and cost-effective alternative to current paper ballot-based voting. It provides many benefits such as increased voter turnout and accuracy in the decision-making process. While presenting many improvements, e-voting still faces serious security challenges that hinder its adoption, especially when designed to be run over mobile devices. In this article, we propose a novel remote e-voting model for large-scale elections by proposing the participation of two conflicting parties to ensure election integrity and accountability. Our scheme can be implemented in IoT devices such as smartphones, which we believe can significantly increase voter turnout of the election process. Our proposed work is secure and preserves voter privacy through secure multiparty computations performed by parties of differing allegiances. It also leverages a blockchain running smart contracts as a publicly accessible and tamper-resistant bulletin board to permanently store votes and prevent double voting. In our security and privacy analysis, we show that our proposed scheme is secure against potential security threats and provides voter anonymity. We show orthogonality between universal verifiability and coercion resistance in our proposed scheme, allowing an election to favor one over the other. Our performance analysis and smartphone simulation results show that the proposed scheme is practical for large-scale elections.

Discrete exponential operation, such as modular exponentiation and scalar multiplication on elliptic curves, is a basic operation of many public-key cryptosystems. However, the exponential operations are considered prohibitively expensive for resource-constrained mobile devices. In this paper, we address the problem of secure outsourcing of exponentiation operations to one single untrusted server. Our proposed secure outsourcing scheme for general exponential (ExpSOS) only requires a very limited number of modular multiplications at local mobile environment, and thus it can achieve significant computational performance gain. ExpSOS also provides a secure verification scheme with probability approximately 1 to ensure that the mobile end users can always receive valid results. The comprehensive analysis as well as the simulation results in real mobile device demonstrates that our proposed ExpSOS can significantly improve the existing schemes in efficiency, security, and result verifiability. We apply ExpSOS to securely outsource several cryptographic protocols to show that ExpSOS can be widely applied to many computation-intensive applications and achieve significant performance improvement.

Cloud computing has changed the way enterprises store, access, and share data. Big data sets are constantly being uploaded to the cloud and shared within a hierarchy of many different individuals with different access privileges. With more data storage needs turning over to the cloud, finding a secure and efficient data access structure has become a major research issue. In this paper, a Privilege-based Multilevel Organizational Data-sharing scheme (P-MOD) is proposed that incorporates a privilege-based access structure into an attribute-based encryption mechanism to handle the management and sharing of big data sets. Our proposed privilege-based access structure helps reduce the complexity of defining hierarchies as the number of users grows, which makes managing healthcare records using mobile healthcare devices feasible. It can also facilitate organizations in applying big data analytics to understand populations in a holistic way. Security analysis shows that P-MOD is secure against adaptively chosen plaintext attack assuming the DBDH assumption holds. The comprehensive performance and simulation analyses using the real U.S. Census Income dataset demonstrate that P-MOD is more efficient in computational complexity and storage space than the existing schemes.