The importance of group communication in the context of the Internet of Things (IoT) is growing, yet the security and stability of this communication are facing significant challenges. The prevailing distributed group key management (GKM) schemes are ill-suited to resource-constrained devices. Furthermore, those that rely on servers are vulnerable to single-point failures and Byzantine risks. The distributed, immutable, and automatic execution of smart contracts on blockchains may offer a potential solution to these problems. This article puts forth a multilayer blockchain-based IoT GKM scheme with Byzantine fault tolerance (BFT). The scheme oversees the management of IoT device subgroups through the deployment of blockchain and smart contracts on edge servers while overseeing the entire device group in a hierarchical structure. A redundant selection mechanism based on hash mapping has been designed to guarantee reliable communication between disparate blockchains and devices. Concurrently, the scheme incorporates a server detection mechanism for Byzantine behavior, thereby ensuring the stability of the blockchain. The results of the experimental analysis demonstrate that the scheme exhibits enhanced security and fault tolerance.

In the cloud era, a large amount of data is uploaded to and processed by public clouds. The risk of privacy leakage has become a major concern for cloud users. Cloud-based video surveillance requires motion detection, which may reveal the privacy of people in a surveillance video. Privacy-preserving video surveillance allows motion detection while protecting privacy. The existing scheme [ 25 ], designed to detect motion on encrypted and H.264-compressed surveillance videos, does not work well on more advanced video compression schemes such as HEVC. In this article, we propose the first motion detection method on encrypted and HEVC-compressed videos. It adopts a novel approach that exploits inter-prediction reference relationships among coding blocks to detect motion regions. The partition pattern and the number of coding bits of each detection block used in prior art are also used to help detect motion regions. Spatial and temporal consistency of a moving object and Kalman filtering are applied to segment connected/merged motion regions, remove noise and background motions, and refine trajectories and shapes of detected moving objects. Experimental results indicate that our detection method achieves high detection recall, precision, and F1-score for surveillance videos of both high and low resolutions with various scenes. It has a similarly high detection accuracy on encrypted and HEVC-compressed videos as that of the existing motion detection method [ 25 ] on encrypted and H.264-compressed videos. Our proposed method incurs no bit-rate overhead and has a very low computational complexity for both motion detection and encryption of HEVC videos.

Privacy-preserving processing is desirable for cloud computing to relieve users' concern of loss of control of their uploaded data. This may be fulfilled with homomorphic encryption. With widely used JPEG, it is desirable to enable JPEG decompression in the homomorphic encryption domain. This is a great challenge since JPEG decoding needs to determine a matched codeword, which then extracts a codeword-dependent number of coefficients. With no access to the information of encrypted content, a decoder does not know which codeword is matched, and thus cannot tell how many coefficients to extract, not to mention to compute their values. In this paper, we propose a novel scheme that enables JPEG decompression in the homomorphic encryption domain. The scheme applies a statically controlled iterative procedure to decode one coefficient per iteration. In one iteration, each codeword is compared with the bitstream to compute an encrypted Boolean that represents if the codeword is a match or not. Each codeword would produce an output coefficient and generate a new bitstream by dropping consumed bits as if it were a match. If a codeword is associated with more than one coefficient, the codeword is replaced with the codeword representing the remaining undecoded coefficients for the next decoding iteration. The summation of each codeword's output multiplied by its matching Boolean is the output of the current iteration. This is equivalent to selecting the output of a matched codeword. A side benefit of our statically controlled decoding procedure is that paralleled Single-Instruction Multiple-Data (SIMD) is fully supported, wherein multiple plaintexts are encrypted into a single plaintext, and decoding a ciphertext block corresponds to decoding all corresponding plaintext blocks. SIMD also reduces the total size of ciphertexts of an image. Experimental results are reported to show the performance of our proposed scheme.