Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical and Electronic Engineering | en_US |
| dc.contributor.advisor | Hu, Haibo (EEE) | en_US |
| dc.creator | Du, Rong | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13653 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | A study on local differential privacy under adverse circumstances | en_US |
| dcterms.abstract | The rapid generation of information in the era of big data has made its analysis and the application of effective strategies increasingly essential across various fields, including business [97], healthcare [33], education [49], transportation [108], and public administration [66]. One method that has proven its immense potential for information gathering is crowdsourcing. However, the convenience of data collection through crowdsourcing also brings significant privacy concerns, particularly under adverse circumstances. | en_US |
| dcterms.abstract | Recent years have witnessed numerous data breach incidents, highlighting the vulnerability of personal information in centralized databases. Notable examples include the Yahoo breaches in 2013 and 2014 affecting 3 billion users [4], the Facebook-Cambridge Analytica scandal impacting over 50 million users [2], the Equifax leak compromising 143 million consumers' data [6], and the Marriott International hotels data breach affecting up to 500 million guests [3]. These incidents underscore the pressing need for robust privacy-preserving mechanisms, especially in adverse data collection environments. | en_US |
| dcterms.abstract | However, LDP faces significant challenges under adverse circumstances, particularly in three key areas: i) The curse of high dimensionality, which compromises aggregation accuracy. ii) Inefficient processing of sparse data with low-frequency values. iii) Vulnerability to Byzantine attacks that introduce poisoned data. | en_US |
| dcterms.abstract | This thesis presents a comprehensive study on enhancing LDP under adverse conditions, making the following contributions: i) We optimize privacy budget allocation among correlated attributes to improve utility in high-dimensional data scenarios. ii) For sparse data, we develop a novel approach using budget allocation and reinforcement learning to identify top-k values efficiently. iii) To combat Byzantine attacks, we establish robust LDP protocols that filter out poisoned data by analyzing varying user behaviors. | en_US |
| dcterms.abstract | Our research advances the field of secure and efficient data analytics under LDP by introducing innovative privacy-preserving mechanisms designed to perform effectively in challenging environments. This study not only addresses current limitations but also provides a foundation for future research in improving LDP's resilience and applicability under adverse circumstances. | en_US |
| dcterms.extent | xiv, 145 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2025 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | open access | en_US |
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