It is essential to design a protocol to allow sensor nodes to attest to their trustworthiness for missioncritical applications based on Wireless Sensor Networks (WSNs). However, it is a challenge to evaluate the trustworthiness without appropriate hardware support. Hence, we present a hardware-based remote attestation protocol to tackle the problem within WSNs. In our design, each sensor node is equipped with a Trusted Platform Module (TPM) which plays the role of a trusted anchor. We start with the formulation of remote attestation and its security. The complete protocol for both single-hop and multi-hop attestations is then demonstrated. Results show the new protocol is effective, efficient, and secure.

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Validation is an alternative approach to the establishment of the trust between two or more entities. The validation allows a challenger to evaluate the trustworthiness of an attesting system based on provided evidence. Early attempts show the evidence is always organized in a linear structure or a balanced tree. These methods, however, are not optimal when facing a complex system and privacy issues. In particular, the Stored Measurement Log (SML) to record the execution history of the attesting system brings efficiency, scalability and privacy problems. We attempt to mitigate them through a new algorithm that uses an unbalanced tree to manage the SML. Leaves represent measurement values of components, and the root is protected by platform configuration registers, which is the same as the balanced tree. The location of each node, however, is constantly mutative until the probability distribution of all components tends towards stabilization. The greater the probability of the leaf is, the closer it is to the root, which benefits the validation. We build a prototype program that is developed through the Trusted Platform Module emulator 0.7. We touch on some mechanisms to reduce the size of the tree and avoid privacy leak. Finally, we perform an in-depth analysis of the validation efficiency, and present the impression of the time complexity of the measurement. Results show that the validation obtains the logarithmic speed-up (O((n/N)lb(n))) and that the time complexity of the measurement process is also acceptable. Besides, our scheme demonstrates advantages in privacy protection and scalability.

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We propose a kind of steganographic algorithm based on the edge processing in this paper. Firstly, the cover is marginalized and reconstructed through mathematical morphology and block markers, then embedding the secret message into the cover picture successfully with F5 steganographic algorithm. The experiments demonstrate that the different cover image use this algorithm to hide the secret information which holds predominant advantages, such as the small changes in image quality, strong ability in anti-attack, and the secret information can be extracted completely from the carrier image. The above advantages make a broad application prospect.

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Behavioral traces of workers have emerged as a new evidence to check the quality of their produced outputs in crowd computing. Whether the evidence is trustworthy or not is a key problem during the process. Challenges will be encountered in addressing this issue, because the evidence comes from unknown or adversarial workers. In this study, we proposed an alternative approach to ensure trustworthy evidence through a hardware-based remote attestation to bridge the gap. The integrity of the evidence was used as the trustworthy criterion. Trusted Platform Module (TPM) was considered the trusted anchor inspired by trusted computing to avoid unreliable or malicious workers. The module carefully recorded and stored many workers’ behavioral traces in the storage measurement log (SML). Each item in the log was extended to a platform configuration register (PCR) by the occurrence sequence of each event. The PCR was a tamper-proof storage inside the TPM. The value of the PCR was also considered evidence together with the SML. The evidence was sent to the crowdsourcing platform with the TPM signature. The platform checked the integrity of the evidence by a series of operations, such as validating the signature and recomputing the SML hash. This process was designed as a remote attestation protocol. The effectiveness, efficiency, and security of the protocol were verified theoretically and through experiments based on the open dataset, WebCrowd25K, and custom dataset. Results show that the proposed method is an alternative solution for ensuring the integrity of behavioral traces.

Fairness plays a vital role in crowd computing by attracting its workers. The power of crowd computing stems from a large number of workers potentially available to provide high quality of service and reduce costs. An important challenge in the crowdsourcing market today is the task allocation of crowdsourcing workflows. Requester-centric task allocation algorithms aim to maximize the completion quality of the entire workflow and minimize its total cost, which are discriminatory for workers. The crowdsourcing workflow needs to balance two objectives, namely, fairness and cost. In this study, we propose an alternative greedy approach with four heuristic strategies to address such an issue. In particular, the proposed approach aims to monitor the current status of workflow execution and use heuristic strategies to adjust the parameters of task allocation. We design a two-phase allocation model to accurately match the tasks with workers. The F-Aware allocates each task to the worker that maximizes the fairness and minimizes the cost. We conduct extensive experiments to quantitatively evaluate the proposed algorithms in terms of running time, fairness, and cost by using a customer objective function on the WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that the F-Aware, which is 1% better than the best competitor algorithm, outperforms other optimal solutions in finding the tradeoff between fairness and cost.