Consecutive Leakage-Resilient and Updatable Lossy Trapdoor Functions and Application in Sensitive Big-Data Environments

Abstract

Lossy trapdoor functions (LTFs) are very useful tools in constructing complex cryptographic primitives in a black-box manner, such as injective trapdoor functions, collision-resistant hashes, CCA secure public-key encryption, and so on. However, the trapdoor is very sensitive in lossy trapdoor function systems, and the attacker can obtain partial sensitive information of trapdoor by the side-channel attacks, which leads to not only the leakage of sensitive information but also the impossibility of provable security . In this paper, we present the new model of updatable lossy trapdoor functions in presence of consecutive and continual leakage-resilient , to provide a more efficient mechanism in solving the sensitive trapdoor leakage problem in LTF systems. Our contribution has threefold: 1) we give the definition and model of consecutive and continual leakage-resilient LTFs, and provide the concrete construction to achieve the lossiness of 50%; 2) using the proposed LTF scheme as a primitive, we present a updatable public-key encryption in the presence of consecutive and continual leakage-resilience, in which the leakage of secret key can occur during the updates that can simulate the real leakage scenarios; and 3) We provide a secure application deployment in sensitive-data revealing environments that employ the proposed $\textsf {CCLR-PKE}$ scheme as a building block, in which a side-channel analyzer might obtain some sensitive information by controlling the secret channel, watching the private memory and detecting the algorithm executing and so on.