Video cameras have been widely deployed for surveillance and smart city. But the authenticity of a video scene faces a great challenge due to the ease of tampering with video data without leaving visible traces. Unfortunately, existing authentication schemes are not efficient for emerging high-resolution videos. In this article, we propose stateful correlation coefficient sampling-based hash (Stateful-CCSH), which adopts correlation coefficient sampling in image hash and learns from previous frames to sample those blocks with more dynamic contents and thus, more likely to be tampered in a video forgery. To decrease the impact of false detection introduced by compression and sampling, we also propose a group smoothing-based authentication scheme. The experimental results show that Stateful-CCSH not only achieves excellent performance in forgery detection, particularly, the detection of moving object removal, but also saves significant computation and communication costs even when the video resolutions are high.

The access frequency pattern leakage reveals sensitive information over encrypted cloud data, such as query inclinations and interests. Even worse, adversaries can infer the content of storage with the help of auxiliary knowledge. It jeopardizes the mutual trust between the client users and the cloud platform as reported in many cases. In this paper, we study the threats model in which adversaries know both the exact in-memory flow of accessed blocks and the processing boundary of each request. Under these settings, he can precisely observe the access frequency patterns in both aggregated and independent perspectives over queries. We then propose the ReFlat module as a counter solution through the K-duplication obfuscation mechanism. ReFlat securely runs inside the hardware enclave provided by Intel SGX and requires no modifications on query processors. The K-duplication mechanism is further optimized with two working functions to practically deal with point and range queries. Comparing with the state-of-the-art schemes using the similar idea, that is, fake query injection, ReFlat eliminates the security risk of involving intermediate proxy and achieves higher robustness under the proposed threat model. We exhibit comparative experiment results showing that ReFlat exceeds existing schemes providing equal security level in multiple system performance metrics.

Many breakthroughs on security and privacy-preserving techniques have emerged to mitigate the trust loss of cloud hosting environment caused by various types of attacks. To enhance memory-level security of multi-keyword fuzzy search, a widely occurred query request, we take the initiative to apply Trusted Execution Environment (a.k.a TEE) technology to our protocol design which provides hardware-based tamper-proof enclaves. Then we propose the Edit Distance-based Obfuscation Mechanism to further protect the query process executed outside TEE against access pattern leakage. With concerns of practicality and performance, we also propose the two-layer fuzzy index structure and Trend-aware Cache. The former addresses the space limitation of TEE memory for searching large datasets, while the latter optimizes the cache utility of TEE with trend-aware coordinator to effectively reduce the communication overhead.