Remote Attestation on Behavioral Traces for Crowd Quality Control Based on Trusted Platform Module. (27th April 2021)

• Record Type: Journal Article
• Authors: Fu, Donglai; Liu, Yanhua
• Other Names: Mostarda Leonardo Academic Editor.
• Is Part Of: Security and communication networks. Volume 2021(2021)
• Publication Date: 2021-04-27
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2021/8859618
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : 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.