Abstract:
In delegated computing, encrypted signal processing can protect sensitive signals from untrusted cloud servers and unauthorized users without affecting the accuracy of large-volume signal analysis and processing.
Most approaches use Paillier’s public key additively homomorphic encryption to encrypt each signal in a large bundle, incurring significant computational costs at local, often resource-constrained devices while guaranteeing only signal input privacy.
This paper proposes PPDWT-1 and PPDWT-2, an efficient privacy-preserving outsourced discrete wavelet transform scheme without public key (fully) homomorphic encryption, to address these limitations. PPDWT-1 protects signal input privacy from the honest-but-curious cloud and unauthorized users, while PPDWT-2 protects coefficient privacy from collusion attacks.
Both constructions encrypt batch signals and process them in the encrypted domain by offlinely executing any one-way trapdoor permutation once. Our discrete wavelet transform results are only decipherable by authorized users.
Our approach has O(1) computational complexity on the user’s end, compared to O(|l|) in state-of-the-art public key homomorphic encryption-based techniques. The expanding factor, upper bound, and privacy-preserving discrete cosine/fourier transform extensions in the encrypted domain are also discussed. Finally, the universal composability (UC) model proves our PPDWT secure. Case studies demonstrate the approach’s efficacy and viability.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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