Confidential computing (CC), designed for security-critical scenarios, uses remote attestation to guarantee code integrity on cloud servers. However, CC alone cannot provide assurance of high-level security properties (e.g., no data leak) on the code. In this paper, we introduce a novel framework, Agora , scrupulously designed to provide a trustworthy and open verification platform for CC. To prompt trustworthiness, we observe that certain verification tasks can be delegated to untrusted entities, while the corresponding (smaller) validators are securely housed within the trusted computing base (TCB). Moreover, through a novel blockchain-based bounty task manager, it also utilizes crowdsourcing to remove trust in complex theorem provers. These synergistic techniques successfully ameliorate the TCB size burden associated with two procedures: binary analysis and theorem proving. To prompt openness, Agora supports a versatile assertion language that allows verification of various security policies. Moreover, the design of Agora enables untrusted parties to participate in any complex processes out of Agora ’s TCB. By implementing verification workflows for software-based fault isolation, information flow control, and side-channel mitigation policies, our evaluation demonstrates the efficacy of Agora .

Quality of Service (QoS) and Quality of Experience (QoE) are employed to characterize network performance to improve network resource utilization in web/internet-based services. However, user credit that also contributes to efficient network resources allocation is ignored. Analyzing users’ behaviors to estimate user credit is a common denominator studied in decades, yet they analyze incomplete attributes of users’ behaviors. A general user credit computing method is urgent and challenging. Thus, we propose Quality of Credit (QoC) and define three metrics to systematically achieve user credit computing. Detailed QoC calculation and differential pricing scheme are proposed to regulate users’ behaviors to improve network resource utilization, which is defined as Credit Level Agreement (CLA). We conduct extensive experiments to demonstrate the effectiveness and feasibility of QoC. Actually, QoS and QoE describe network performance metrics, while QoC depicts the credibility of users, so that they can complement each other, and jointly provide support for collaborative computing and various applications at a higher perspective than ever.

As the gig economy continues to grow, behaviors of workers on gig service platforms have an increasing impact on service satisfaction. For example, fatigue driving behaviors of drivers in ride-hailing platforms may cause serious damages, both for individuals and society. Therefore, regulating behaviors of workers is urgent and challenging. A lot of studies are conducted to detect workers' noncompliance behaviors, such as detecting fatigue driving by computer vision or pattern recognition methods. However, few of them indicate how to efficiently exploit the detection results to regulate workers' behaviors. In this paper, we point out that workers' noncompliance behaviors and their incomes should be correlated, and propose a quantifiable computation framework that includes a price-based incentive mechanism and a method to verify the effectiveness of the mechanism. Historical behaviors of workers are summarized as credits and stored in nonfungible token called CreditToken to ensure that it cannot be tampered with. CreditToken will further affect workers' incomes. We abstract the decision-making behavior of workers as a Markov decision process and demonstrate the effectiveness of the incentive mechanism with model checking and formal methods. The analysis shows that our framework is able to provide a rational price strategy formation for gig service platforms, and can be flexibly integrated into existing pricing schemes to maximize the value of the detection results. Extensive experiments illustrate the advanced nature and practicality of our framework.

The popularity of electric vehicles depends on convenient and efficient charging services. At present, none of existing charging services allow users to reach charging stations at desirable time and charge immediately when they arrive without waiting. This paper proposes a charging reservation service approach based on the consortium blockchain and smart contract technology. Users can choose the charging station and charging time period with no charging congestion, which is based on the charging information recorded in the consortium blockchain composed of stations located in distributed regions in a city. To ensure a user arrives at the charging station on time and charge within due time as he/she has reserved, a personalized pricing scheme for reward and punishment by utilizing smart contract is proposed. We take the past charging behavior into consideration when deciding current charging price of each user, which can provide individualized prices for different users. This approach can not only greatly reduce the user's waiting time, but also offer high cost-effective charging services for good behavior users. We carry out experimental verification under multiple sets of parameter settings, illustrate the variations in three aspects including user's initial score, violation rate and intensity of reward and punishment, thus the feasibility of our approach is proved. Our work is a credible charging paradigm based on trust mechanism via blockchain, which has the potential to become an industry service standard for electric vehicle charging.