IoT Services:Realizing Private Real-Time Detection via Authenticated Conjunctive Searchable Encryption

With the rapid development of wireless communication technology,the Internet of Things is playing an increasingly important role in our everyday.The amount of data generated by sensor devices is increasing as a large number of connectable devices are deployed in many fields,including the medical,agricultural,and industrial areas.Uploading data to the cloud solves the problem of data overhead but results in privacy issues.Therefore,the question of how to manage the privacy of uploading data and make it available to be interconnected between devices is a crucial issue.In this paper,we propose a scheme that supports real-time authentication with conjunctive keyword detection(RA-CKD),this scheme can realize the interconnection of encrypted data between devices while ensuring some measure of privacy for both encrypted data and detection tokens.Through authentication technology,connected devices can both authenticate each other’s identity and prevent malicious adversaries from interfering with device interconnection.Finally,we prove that our scheme can resist inside keyword guessing attack through rigorous security reduction.The experiment shows that the efficiency of RA-CKD is good enough to be practical.

Fast feature matching based on r-nearest k-means searching

Feature matching has been frequently applied in computer vision and pattern recognition.In this paper,the authors propose a fast feature matching algorithm for vector-based feature.Their algorithm searches r-nearest neighborhood clusters for the query point after a k-means clustering,which shows higher efficiency in three aspects.First,it does not reformat the data into a complex tree,so it shortens the construction time twice.Second,their algorithm adopts the r-nearest searching strategy to increase the probability to contain the exact nearest neighbor(NN)and take this NN as the global one,which can accelerate the searching speed by 170 times.Third,they set up a matching rule with a variant distance threshold to eliminate wrong matches.Their algorithm has been tested on large SIFT databases with different scales and compared with two widely applied algorithms,priority search km-tree and random kd-tree.The results show that their algorithm outperforms both algorithms in terms of speed up over linear search,and consumes less time than km-tree.Finally,they carry out the CFI test based on ISKLRS database using their algorithm.The test results show that their algorithm can greatly improve the recognition speed without affecting the recognition rate.