Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"privacy preserving machine learning"”.
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Interpretability and Debugging for Distributed Privacy Preserving Machine Learning
Machine learning systems increasingly rely on privacy-preserving distributed training to leverage sensitive data across multiple organizations without centralization. Federated Learning (FL), a distributed privacy-preserving machine learning paradigm, enables hospitals, devices, and enterprises to …
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A system for privacy-preserving machine learning on personal data
… of a system which allows users to generate machine learning models with their own data while preserving privacy. We approach the problem in two steps. First, we present a framework with which a user can collate personal data from a variety of sources in order to generate machine learning …
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Tiresias : a peer-to-peer platform for privacy preserving machine learning
… to offer computations on their data in a privacy-preserving way and for requesters -- i.e. anyone who can benefit from applying machine learning to the users' data -- to request computations on user data they would otherwise not be able to collect. Through carefully designed differential …
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Privacy Modelling and Preservation for Assisted Living within Smart Homes
… solutions typically combine IoT technologies and machine learning to provide services that are context-aware and personalised. However, modern machine learning and internet of things systems present new challenges in privacy and security. With the collection of large datasets and increasingly …
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Essays on the Decision Value of Data in Marketing Measurement and Targeting
… is limited by heterogeneity, identification, and privacy constraints. Chapter 2 develops a model of actionable heterogeneity and shows that heterogeneity alone is not enough for personalization to outperform the best uniform policy. The chapter characterizes how within-treatment heterogeneity, …
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Neural Data Shaping and Evaluation via Mutual Information Estimation
Machine learning in sensitive domains like healthcare currently faces a major bottleneck due to the scarcity of data that is publicly available. Privacy protection regulations such as HIPAA and GDPR and recent progress in information estimation literature motivate us to investigate the issue from …
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Synthesizing tabular data using conditional GAN
… including data compression, data disclosure, and privacy-preserving machine learning. However, because tabular data usually contains a mix of discrete and continuous columns, building such a model is a non-trivial task. Continuous columns may have multiple modes, while discrete columns are …
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Split learning on FPGAs
… or flexibility. At the same time, the field of machine learning is diversifying to include distributed deep learning methods like split learning, which can help preserve privacy by avoiding the sharing of raw data and model details. In order to continue to expand the capabilities of …
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Secure and scalable robust federated learning
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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Utility-driven optimization and placement framework for Visual IoT analytics over edge-cloud environments
… price-efficient manner as well as protecting the privacy and confidentiality of users' sensitive data against misuse by the edge/cloud provider. We address the above challenges by: (1) building a framework for processing visual data streams across edge and cloud compute resources, (2) developing …
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The fundamental limits of statistical data privacy
… need to share data and the need to preserve the privacy of Internet users. The need for privacy appears in three main contexts: (1) the global privacy context, as in when private companies and public institutions release personal information about individuals to the public; (2) the local privacy …