Searchable Encryption for Integrating Cloud and Sensor Networks with Secure Updates
Selasi Kwame Ocansey and Changda Wang
Integrating cloud with sensor networks allows for the possibility to solve sensor networks’ limited computing power and capacity problems, as well as the storage and processing of the acquired data, without increasing the sensor networks’ costs excessively. Indeed, the massive amount of highly sensitive data created and collected by sensor networks poses a number of challenges that current architectures are unable to address. A major challenge is how to securely search on the sensitive sensor data on the cloud. Searchable symmetric encryption (SSE) is a key technique which enables searching on the cloud data. Hence, SSE must provide for the functions of effective, secure privacy and usability when searching on the sensor collected data on the cloud. In this framework, we leverage a practicable scheme which supports secure and updatable operations such as data and data indexes updating on the cloud. We construct an efficient multi-keyword search scheme using k-nearest neighbor (kNN) and Bloom Filter(BF) to achieve ranked search scheme as well as multi-keyword searching. Security analysis shows that our scheme is able to achieve: Unlinkability of Trapdoors, Forward and Backward Privacy, and hidden access policy, etc. The communication over-head experiment shows that our scheme is improved by 50% in terms of efficiency than that of the related works. That of the computation overhead demonstrates the search time and at-tributes revocation functionalities are 15% and 35% efficient when compared to the existing works.
Keywords: Searchable Encryption, Cloud Computing, Data Updating, Secure Privacy, Sensor Networks, Ranked Search