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 20 of 85 for “"differential privacy"”.
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Visualization and differential privacy
Privacy-preserving statistical databases are designed to provide information about a population while preventing end-users from learning about an individual. Meanwhile, scholars have shown that a sophisticated adversary can break such assumption against primitive privacy protections. Differential …
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Post-processing in Differential Privacy
In recent years, the prominence of data privacy concerns has surged alongside the unprecedented growth in large-scale data collection and analysis. Since its introduction in 2006, differential privacy has rapidly emerged as the gold standard for addressing these escalating privacy challenges in …
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Differential Privacy in Reinforcement Learning
… information, the security of policies and privacy preservation in reinforcement learning have given rise to widespread concerns. In addition, deep reinforcement learning policies parameterized by neural networks have been demonstrated to be vulnerable to adversarial attacks in supervised …
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Statistical learning with differential privacy
… growth of data, the preservation of individual privacy has become a prominent challenge in data-driven decision-making across diverse domains. The concept of differential privacy, a robust mathematical framework, has emerged as the gold standard for providing rigorous data privacy protections. …
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The optimal mechanism in differential privacy
Differential privacy is a framework to quantify to what extent individual privacy in a statistical database is preserved while releasing useful aggregate information about the database. This dissertation studies the fundamental trade-off between privacy and utility in differential privacy in the …
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Automated methods for checking differential privacy
Differential privacy is a de facto standard for statistical computations over databases that contain private data. The strength of differential privacy lies in a rigorous mathematical definition which guarantees individual privacy and yet allows for accurate statistical results. Thanks to its …
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Local differential privacy in decentralized optimization
Privacy concerns with sensitive data are receiving increasing attention. In this thesis, we study local differential privacy (LDP) in interactive decentralized optimization. Comparing to central differential privacy (DP), where a centralized curator maintains the dataset, LDP is a stronger notion …
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Improving the adaptability of differential privacy
Differential privacy is a mathematical technique that provides strong theoretical privacy guarantees by ensuring statistical indistinguishability of individuals in a dataset. It has become the de facto framework for providing privacy-preserving data analysis over statistical datasets. Differential …
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Studies in Differential Privacy and Federated Learning
… focus is the advancement of two fields of data privacy: Differential Privacy and Federated Learning. Differential Privacy is one of the most successful modern privacy methods. By injecting carefully structured noise into a dataset, Differential Privacy obscures individual contributions while …
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Achieving Differential Privacy and Fairness in Machine Learning
… machine learning algorithms on matters like privacy and fairness. Currently, many studies only focus on protecting individual privacy or ensuring fairness of algorithms separately without taking consideration of their connection. However, there are new challenges arising in privacy preserving …
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Privacy-aware Federated Learning with Global Differential Privacy
… information can still be revealed. To combat privacy attacks on the FL systems, various attempts have been made to incorporate differential privacy within the framework. In this thesis, we investigate the trade-offs between communication costs and training variance under a Federated Learning …
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Statistical verification and differential privacy in cyber-physical systems
… thesis studies the statistical verification and differential privacy in Cyber-Physical Systems. The first part focuses on the statistical verification of stochastic hybrid system, a class of formal models for Cyber-Physical Systems. Model reduction techniques are performed on both Discrete-Time …
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Case Studies in Differential Privacy for Computer Networking Research
We conduct two case studies on the use of differential privacy in computer networking research: private analysis of 1) Internet performance measurements from the Measuring Broadband America dataset and 2) flow-based network traces from the NF-UNSW-NB15 Netflow dataset. We survey two open-source …
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Synthesizing Linked Data and Detecting Per-Query Gaps Under Differential Privacy
… value at scale has raised concerns about privacy protections. Formal policies have made access to such data heavily regulated, often resulting in users waiting months or years before they can even start analyzing the data to determine fit for their tasks. In the recent past, generation of …
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Differential privacy in the era of generative AI: promises and challenges
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms
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Converting to Optimization in Machine Learning: Perturb-and-MAP, Differential Privacy, and Program Synthesis
… a database containing sensitive data in a safe ("differentially private") manner can be converted into an optimization problem using the theory of Reproducing Kernel Hilbert Spaces. Finally, the fourth case study casts the challenging discrete search problem of program synthesis from input-output …
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Implementing Differential Privacy for Privacy Preserving Trajectory Data Publication in Large-Scale Wireless Networks
… to these outside researchers poses a threat to privacy of users. The dueling need for utility and privacy must be addressed. This thesis studies the concept of differential privacy for fulfillment of these goals of releasing high utility data to researchers while maintaining user privacy. The …
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A Universally Applicable Differential Privacy System: Redefining Utility in Database Privacy to Prioritize User Experience
Data privacy is a fundamental ethical goal. We must aim for innovating without exploiting. In order to provide formal privacy guarantees, differential privacy has been the central method of implementing database privacy. However, there are many barriers to widespread adoption. General methods lack …
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Privacy-preserving social network analysis
<p>Data privacy in social networks is a growing concern that threatens to limit access to important information contained in these data structures. Analysis of the graph structure of social networks can provide valuable information for revenue generation and social science research, but …
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