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24 April 2020 A new tool for detecting tampering of big data programs
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We propose a tool called BigHASH for efficiently detecting tampering of big data programs (e.g., by malware) when executed in a private cluster or a public cloud environment. BigHASH produces the execution metadata of a program that precisely captures the critical internal data structures and content of the program (at runtime) using graph algorithms and homomorphic hashing. Homomorphic hashing provides two key benefits: (a) It enables parallel hash computation for efficiency. (b) It provides the ability to cope with cluster environments containing different number of servers when executing the program. BigHASH uses a blockchain network to store the execution metadata of programs as it provides a decentralized, secure, tamper-proof storage. To detect whether a program has been tampered or not during execution, BigHASH compares the execution metadata published by the owner (in a trusted environment) on the blockchain network to that produced by a user in his/her cluster environment. BigHASH is simple to use and provides automatic code instrumentation so that a programmer is not burdened to write any extra code to use BigHASH.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Praveen Rao, Srini Bhagavan, and Laurent Njilla "A new tool for detecting tampering of big data programs", Proc. SPIE 11417, Cyber Sensing 2020, 114170J (24 April 2020);

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