Attribute Based Homomorphic Encryption (ABHE) Scheme For Outsourced Big Data Computation

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dc.contributor.authorTan, Soo Fun
dc.date.accessioned2018-01-10T07:26:51Z
dc.date.available2018-01-10T07:26:51Z
dc.date.issued2017-09
dc.description.abstractOutsourced Big Data computation raises several security and privacy issues such as data breaches, identity theft, as well as malicious insider threats, from espionage to sabotage. In this study, Attribute Based Homomorphic Encryption (ABHE) scheme is proposed as a mechanism to protect confidentiality and privacy issues of outsourced Big Data computations. The Homomorphic Encryption scheme is a cryptosystem that is capable of computing on encrypted data, whereas the Attribute Based Encryption scheme is a specialization of Public Key Encryption that realises the implicit access control on encrypted data without the engagement of conventional trusted server. Therefore, the proposed ABHE scheme is a promising tool that is capable of performing computations on encrypted data, meanwhile providing access control on these private data directly, thus making end-to-end data protection for outsourced Big Data computation possible. However, the construction of ABHE scheme always affecting the computation capabilities of Homomorphic Encryption scheme. Moreover, existing schemes still suffering from practical deployment issues such as slow running speed, and huge ciphertext size, as well as their applications are only considered the single–data owner scenario. To bridge these gaps, this study is subsequently divided into six phases, includes problem formulation and preliminary investigation, algorithms design I, algorithms design II, algorithms design III, security analysis and lastly, experimental development and performance assessment. For supporting a multi-user environment of outsourced Big Data computation, the algorithm design I focus on incorporating Attribute Based Encryption (ABE) scheme onto Homomorphic Encryption (HE) scheme, with the aim i.e. without affecting the capability of homomorphic computation. To support the high volume of outsourced Big Data processing, the algorithm design II aimed to reduce generated ciphetext size in the proposed ABHE scheme. Subsequently, the proposed ABHE scheme is further extended into non-circuit based approach in algorithm design III in order to improve its computation time for supporting high velocity of outsourced Big Data computations. Theoretical and experimental results shown that the proposed non-circuit based ABHE scheme has greatly reduced the computation time and ciphertext size as compared to circuit based approach. For instance, the proposed non-circuit based ABHE scheme took approximately 8.07 milliseconds and 1.64049 seconds, as compared to recent HE scheme, which took approximately 8.14 milliseconds and 1.67247 seconds, for performing a single additive and multiplicative homomorphism respectively. Subsequently, the encryption of 4 megabytes’ of data that generates a ciphertext more than 280 gigabytes in recent study is further reduced to 130 gigabytes in the proposed ABHE scheme, indeed, with the added capability of controlling access on these encrypted data implicitly. Subsequently, the proposed ABHE scheme was proven semantically secure under Indistinguishable under Non-Adaptive Chosen Ciphertext attack (INDCCA1) and collusion attack with the hardness of Decision Ring-LWEd,q,𝓍 problem.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/5358
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectAttribute Based Homomorphic Encryption as a mechanismen_US
dc.subjectto protect confidentiality outsourced Big Data computationsen_US
dc.titleAttribute Based Homomorphic Encryption (ABHE) Scheme For Outsourced Big Data Computationen_US
dc.typeThesisen_US
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