A Study on Generic Data Analysis Application the Cloud Using SGX

Abstract

The analysis itself however in the midst of preparation and post-processing steps. Handling information returning directly from the supply, e.g. a sensor, typically needs preconditioning like parsing and removing orthogonal data before data processing algorithms will be applied to research the info. complete data processing frameworks generally don't offer such parts since they need a currency computer file format. what is more, they're typically restricted to the offered algorithms or a speedy integration of latest algorithms for the aim of fast testing isn't attainable. to deal with this disadvantage, we have a tendency to gift the info analysis framework Knowing, that is well extendable with extra algorithms by exploitation associate OSGi compliant design. the software package and therefore the hypervisor out of the TCB; therefore, confidentiality and integrity square measure preserved albeit these massive parts square measure compromised. VC3 depends on SGX processors to isolate memory regions on individual computers, and to deploy new protocols that secure distributed MapReduce computations. VC3 optionally enforces region self-integrity invariants for all MapReduce code running inside isolated regions, to stop attacks thanks to unsafe memory reads and writes. Experimental results on common benchmarks show that VC3 performs well compared with unprotected Hadoop: VC3's average runtime overhead is negligible for its base security guarantees, 4.5% with write integrity and eight with read/write integrity.