The widespread use of personal devices and dedicated recording facilities has led to the generation of massive amounts of personal information or data. Some of them are high-dimensional and... Show moreThe widespread use of personal devices and dedicated recording facilities has led to the generation of massive amounts of personal information or data. Some of them are high-dimensional and unstructured data, such as video and location data. Analyzing these data can provide significant benefits in real-world scenarios, such as videos for monitoring and location data for traffic analysis. However, while providing benefits, these complicated data always raise serious privacy concerns since all of them involve personal information. To address privacy issues, existing privacy protection methods often fail to provide adequate utility in practical applications due to the complexity of high-dimensional and unstructured data. For example, most video sanitization techniques merely obscure the video by detecting and blurring sensitive regions, such as faces, vehicle plates, locations, and timestamps. Unfortunately, privacy breaches in blurred videos cannot be effectively contained, especially against unknown background knowledge. In this thesis, we propose three different differentially private frameworks to preserve the utility of video and location data (both are high-dimensional and unstructured data) while meeting the privacy requirements, under different well-known privacy settings. Specifically, to our best knowledge, wepropose the first differentially private video analytics platform (VideoDP) which flexibly supports different video queries or query-based analyze with a rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Secondly, we define a novel privacy notion ϵ-Object Indistinguishability for all the predefined sensitive objects (e.g., humans, vehicles) in the video, and then propose a video sanitization technique VERRO that randomly generates utility-driven synthetic videos with indistinguishable objects. Therefore, all the objects can be well protected in the generated utility-driven synthetic videos which can be disclosed to any untrusted video recipient. Third, we propose the first strict local differential privacy (LDP) framework for location-based service (LBS) (“L-SRR”) to privately collect and analyze user locations or trajectories with ε-LDP guarantees. Specifically, we design a novel LDP mechanism “staircase randomized response” (SRR) and extend the empirical estimation to further boost the utility for a diverse set of LBS Apps (e.g., traffic density estimation, k nearest neighbors search, origin-destination analysis, and traffic-aware GPS navigation). Finally, we conduct experiments on real videos and location dataset, and the experimental results demonstrate all frameworks can have good performance. Show less
