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Towards Utility-Driven Data Analytics with Differential Privacy
Title
Towards Utility-Driven Data Analytics with Differential Privacy
Creator
Wang, Han
Date
2023
Description
The widespread use of personal devices and dedicated recording facilities has led to the generation of massive amounts of personal information...
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.
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Towards Trustworthy Multiagent and Machine Learning Systems
Title
Towards Trustworthy Multiagent and Machine Learning Systems
Creator
Xie, Shangyu
Date
2022
Description
This dissertation aims to systematically research the "trustworthy" Multiagent and Machine Learning systems in the context of the Internet of...
Show moreThis dissertation aims to systematically research the "trustworthy" Multiagent and Machine Learning systems in the context of the Internet of Things (IoT) system, which mainly consists of two aspects: data privacy and robustness. Specifically, data privacy concerns about the protection of the data in one given system, i.e., the data identified to be sensitive or private cannot be disclosed directly to others; robustness refers to the ability of the system to defend/mitigate the potential attacks/threats, i.e., maintaining the stable and normal operation of one system.Starting from the smart grid, a representative multiagent system in the IoT, I demonstrate two works on improving data privacy and robustness in aspects of different applications, load balancing and energy trading, which integrates secure multiparty computation (SMC) protocols for normal computation to ensure data privacy. More significantly, the schemes can be readily extended to other applications in IoT, e.g., connected vehicles, mobile sensing systems.For the machine learning, I have studied two main areas, i.e., computer vision and natural language processing with the privacy and robustness correspondingly. I first present the comprehensive robustness evaluation study of the DNN-based video recognition systems with two novel proposed attacks in both test and training phase, i.e., adversarial and poisoning attacks. Besides, I also propose the adaptive defenses to fully evaluate such two attacks, which can thus further advance the robustness of system. I also propose the privacy evaluation for the language systems and show the practice to reveal and address the privacy risks in the language models. Finally, I demonstrate a private and efficient data computation framework with the cloud computing technology to provide more robust and private IoT systems.
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