التفاصيل البيبلوغرافية
| العنوان: |
Survey: Leakage and Privacy at Inference Time. |
| المؤلفون: |
Jegorova M, Kaul C, Mayor C, O'Neil AQ, Weir A, Murray-Smith R, Tsaftaris SA |
| المصدر: |
IEEE transactions on pattern analysis and machine intelligence [IEEE Trans Pattern Anal Mach Intell] 2023 Jul; Vol. 45 (7), pp. 9090-9108. Date of Electronic Publication: 2023 Jun 05. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: IEEE Computer Society Country of Publication: United States NLM ID: 9885960 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1939-3539 (Electronic) Linking ISSN: 00985589 NLM ISO Abbreviation: IEEE Trans Pattern Anal Mach Intell Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: |
Original Publication: [New York] IEEE Computer Society. |
| مستخلص: |
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance since commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malicious leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. We then propose a taxonomy across involuntary and malicious leakage, followed by description of currently available defences, assessment metrics, and applications. We conclude with outstanding challenges and open questions, outlining some promising directions for future research. |
| تواريخ الأحداث: |
Date Created: 20230404 Date Completed: 20230606 Latest Revision: 20230606 |
| رمز التحديث: |
20231215 |
| DOI: |
10.1109/TPAMI.2022.3229593 |
| PMID: |
37015684 |
| قاعدة البيانات: |
MEDLINE |
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