Trusted Sampling-Based Result Verification on Mass Data Processing

• Main Authors: Yan Ding, Huaimin Wang, Peichang Shi, Hongyi Fu, Changguo Guo, Muhua Zhang
• Format: Conference Proceeding
• Language: English
• Subjects: Cloud computing; Computational efficiency; Computational modeling; Data models; Data processing; MapReduce; mass data processing; Merkle tree; result verification; trusted sampling; Vegetation; Watermarking
• DOI: 10.1109/SOSE.2013.65

Computation integrity is difficult to verify when mass data processing is outsourced. Current integrity protection mechanisms and policies verify the results generated by participating nodes within a computing environment of service providers (SP), which can not preventing the subjective cheating of SPs. This paper provides an analysis and a modeling of computation integrity for mass data processing services. A third-party sampling-result verification method called trusted sampling-based third-party result verification (TS-TRV) is proposed to prevent lazy cheating by SPs. TS-TRV is a general solution for common computing jobs and uses the powerful computing capability of SPs to support verification computing, thus lessening the computing and transmission burden of the verifier. A series of simulation experiments and theoretical analysis indicates that TS-TRV is an effective method of detecting the cheating behavior of SP while ensuring the authenticity of sampling. Compared with the transmission overhead of naive sampling verification, which is O(N), the network transmission overhead of TS-TRV is only O(logN). TS-TRV efficiently solves the verification problem of the intermediate results in MapReduce-based mass data processing.