Blockchain technology has become a popular choice for electronic voting systems due to its transparency, security, and decentralization. However, it is not a perfect solution, as its inherent immutability poses challenges in blockchain‐based e‐voting systems. Specifically, without the physical security provided by traditional polling stations, preventing bribery and coercion becomes more difficult. Additionally, because of blockchain’s immutability, voters who are coerced or mistakenly vote cannot correct their choice. To address these issues, this paper proposes a secure blockchain‐based voting system with editable ballots. The system uses chameleon hashes with ephemeral trapdoors and a timestamp mechanism, allowing voters to modify their ballots within a legitimate timeframe. Additionally, a modified Paillier cryptosystem and blind signature technology are used to ensure that any modifications leave no trace. We simulate and evaluate the system using Fabric 2.2, focusing on computational complexity and system stability. Analysis of experimental results shows that the blockchain‐based voting system with an editable ballot mechanism proposed in this article has good computational cost and stability performance under normal use pressure.

In cloud computing, users need to authenticate to access various resources. Attribute-based anonymous credentials (ABCs) provide a tool for privacy-preserving authentication, allowing users to prove possession of a set of attributes to cloud service providers anonymously. Most existing works on ABC deal with credentials on attributes issued by a single authority (issuer). In reality, it is more practical for users to obtain credentials on attributes from multiple authorities. There are a few works on multi-authority ABC, which do not support delegation needed in real deployments. In this article, we present the first delegatable multi-authority attribute-based anonymous credential system, which simultaneously achieves revocation and traceability. We also give the security analysis of our construction. Finally, we implement our system, and the experimental results show its efficiency.

Incentive-based applications enable users to obtain rewards after they complete a task, but how to balance the privacy and accountability is one of the most serious concerns currently. Publicly accountable anonymous authentication provides an excellent way verifying a user’s identity in a privacy-preserving way while ensuring public accountability in case of dispute. Although different kinds of these schemes have been proposed, they all assume that there is a single centralized certificate authority issuing a certificate to users, and are not suitable for an actual scenario which always involves multiple authorities. Therefore, a new primitive called multi-authority linkable and traceable anonymous authentication is proposed to address this issue, enabling privacy protection while holding public accountability under a multi-authority setting. We formally define a security model for this new notion and simultaneously design a generic construction while giving the security proof. Additionally, we implement the proposed scheme to show its efficiency.

Numerous anonymous authentication schemes are designed to provide efficient authentication services while preserving privacy. Such schemes may easily neglect access control and accountability, which are two requirements that play an important role in some particular environments and applications. Prior designs of attribute-based anonymous authentication schemes did not concentrate on providing full anonymity while at the same time holding public traceability. To address this problem, we formally define and present a new primitive called traceable attribute-based anonymous authentication (TABAA) which achieves (i) full anonymity, i.e., both registration and authentication cannot reveal user’s privacy; (ii) reusable credential, i.e., a registered credential can be repeatedly used without being linked; (iii) access control, i.e., only when the user’s attribute satisfies the access policy can the user be involved in authentication; and (iv) public traceability, i.e., anyone, without help from the trusted third party, can trace a misbehaving user who has authenticated two messages corresponding to a common address. Then, we formally define the security requirements of TABAA, including unforgeability, anonymity, and accountability, and give a generic construction satisfying the security requirements. Furthermore, based on TABAA, we propose the first attribute-based, decentralized, fully anonymous, publicly traceable e-voting, which enables voters to engage in a number of different voting activities without repeated registration.

We explore the problem of placing object replicas on nodes in a distributed system to maximize the number of objects that remain available when node failures occur. In our model, failing (the nodes hosting) a given threshold of replicas is sufficient to disable each object, and the adversary selects which nodes to fail to minimize the number of objects that remain available. We specifically explore placement strategies based on combinatorial structures called t-packings, provide a lower bound for the object availability they offer, show that these placements offer availability that is c-competitive with optimal, propose an efficient algorithm for computing combinations of t-packings that maximize their availability lower bound, and provide parameter selection strategies to concretely instantiate our schemes for different system sizes. We compare the availability offered by our approach to that of random replica placement, owing to the popularity of the latter approach in previous work. After quantifying the availability offered by random replica placement in our model, we show that our combinatorial strategy yields placements with better availability than random replica placement for many realistic parameter values.

This article presents StopWatch, a system that defends against timing-based side-channel attacks that arise from coresidency of victims and attackers in infrastructure-as-a-service clouds. StopWatch triplicates each cloud-resident guest virtual machine (VM) and places replicas so that the three replicas of a guest VM are coresident with nonoverlapping sets of (replicas of) other VMs. StopWatch uses the timing of I/O events at a VM’s replicas collectively to determine the timings observed by each one or by an external observer, so that observable timing behaviors are similarly likely in the absence of any other individual, coresident VMs. We detail the design and implementation of StopWatch in Xen, evaluate the factors that influence its performance, demonstrate its advantages relative to alternative defenses against timing side channels with commodity hardware, and address the problem of placing VM replicas in a cloud under the constraints of StopWatch so as to still enable adequate cloud utilization.

This paper presents StopWatch , a system that defends against timing-based side-channel attacks that arise from coresidency of victims and attackers in infrastructure-as-a-service clouds. StopWatch triplicates each cloud-resident guest virtual machine (VM) and places replicas so that the three replicas of a guest VM are coresident with nonoverlapping sets of (replicas of) other VMs. StopWatch uses the timing of I/O events at a VM's replicas collectively to determine the timings observed by each one or by an external observer, so that observable timing behaviors are similarly likely in the absence of any other individual, coresident VM. We detail the design and implementation of StopWatch in Xen, evaluate the factors that influence its performance, and address the problem of placing VM replicas in a cloud under the constraints of StopWatch so as to still enable adequate cloud utilization.

Host-based anomaly detectors monitor the control-flow and data-flow behavior of system calls to detect intrusions. Control-flow-based detectors monitor the sequence of system calls, while data-flow-based detectors monitor the data propagation among arguments of system calls. Besides pointing out that data-flow-based detectors can be layered on top of control-flow-based ones (or vice versa) to improve accuracy, there is a large gap between the two research directions in that research along one direction had been fairly isolated and had not made good use of results from the other direction. In this paper, we show how data-flow analysis can leverage results from control-flow analysis to learn more accurate and useful rules for anomaly detection. Our results show that the proposed control-flow-analysis-aided data-flow analysis reveals some accurate and useful rules that cannot be learned in prior data-flow analysis techniques. These relations among system call arguments and return values are useful in detecting many real attacks. A trace-driven evaluation shows that the proposed technique enjoys low false-alarm rates and overhead when implemented on a production server.