Identity-based outsider anonymous cloud data outsourcing with simultaneous individual transmission for IoT environment

摘要

The integration of the Internet of Things (IoT) and cloud computing has become an attractive cloud-oriented big data processing paradigm, which is playing an important role in efficiency and productivity for digitalization of numerous IoT enabled industries. However, cloud-assisted IoT is also becoming an increasingly attractive target for various cyber-attacks, including the authenticity of outsourcing data, untrustworthiness of third parties, and data security and privacy. As a potential and promising solution to securely outsource data, we present the first construction for an efficient cloud data outsourcing system with simultaneous individual transmission preserving outsider anonymity of the subscribed consumer set. In our system, a data owner generates personalized data for each of the consumers in a group and transmits a common encrypted data for the group in such a way that the subscribed consumer set is completely hidden from the outsiders. Personalized data can be recovered only by an authorized consumer, while the common data can be decrypted by all the authorized consumer in that group. The communication bandwidth is compact in our construction, and the decryption algorithm requires significantly less computation cost. We design our scheme using asymmetric bilinear map over the prime order group to prevent fault attacks on symmetric bilinear map. Our construction is built in identity based setting without any non-standard q -type security assumption and does not use random oracles. Our scheme enjoys adaptive security against an indistinguishable chosen-plaintext attack under the hardness of the standard decisional bilinear Diffie-Hellman exponent problem. Furthermore, our design supports an exponential number of consumers as the size of the valid identity set grows exponentially with the security parameter, whereas it is only polynomial in the security parameter for the existing cloud data outsourcing systems. In particular, the implementation and performance analysis explicates the advantages of our design for resource-constrained IoT enabled frameworks.