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Additively Homomorphic IBE with Auxiliary Input for Big Data Security

摘要


Additively homomorphic encryption is a relaxed notion of homomorphic encryption, which enables us to compute linear functions over the encrypted data. Additively homomorphic identity-based encryption (IBE) is an efficient resolution tool for the problem of security with privacy in the big data applications. In this paper, we design a leakage resilient additive homomorphic IBE scheme with auxiliary input to resist side-channel attacks for the end users. We prove that our scheme is auxiliary input chosen-plaintext attack (AI-CPA) secure, and test our scheme over the resource-constrained Intel Edison Platform. Both theoretical analysis and experimental result show that our scheme is very suitable for aggregating data submitted from the end users, who may be at the risk of leaking their secret keys.

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