Towards leakage-resilient fine-grained access control in fog computing

Fog Computing, a technology that takes advantage of both the paradigms of Cloud Computing and the Internet of Things, has a great advantage in reducing the communication cost. Since its introduction, fog computing has found a lot of applications, including, for instance, connected vehicles, wireless sensors, smart cities and etc. One prominent problem in fog computing is how fine-grained access control can be imposed. Functional encryption, a new cryptographic primitive, is known to support fine-grained access control. However, when it comes to some new threats in the fog computing scenario, such as side channel attacks, functional encryption cannot maintain its security. Therefore, we need new cryptographic primitives that not only provide a way to securely share data with a fine-grained access control but also are able to resist those new threats. In this paper, we consider how to construct functional encryption schemes (FEs) adaptively secure in continual memory leakage model (CML), which is one of the strongest models that allows continuous leakage on both user and master secret keys. Besides providing privacy and fine-grained access control in fog computing, our scheme can also guarantee security against side channel attacks. More concretely, we propose a generic framework for constructing fully secure leakage-resilient FEs (LR-FEs) in the CML model results from leakage-resilient pair encoding, which is an extension of pair encoding presented in the recent work of Attrapadung. In this way, our framework simplifies the design and analysis of LR-FEs into the design and analysis of predicate encodings. Moreover, we discover new adaptively secure LRFEs, including FE for regular languages, attribute-based encryption (ABE) for large universe and ABE with short ciphertext. Above all, leakage-resilient adaptively secure functional encryption schemes can equip fog computing with higher security and fine-grained access control. (C) 2017 Elsevier B.V. All rights reserved.