Optimal Lower Bound for Differentially Private Multi-party Aggregation
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Detection methods exploit activation statistics or model properties to determine whether a model is backdoored or whether a training/test example is a backdoor example.
“Detection methods exploit activation statistics or model properties to determine whether a model is backdoored [151], [152], or whether a training/test example is a backdoor example [47].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [152]
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- Authors
- Chan, T.-H. H., Shi, E., & Song, D.
- Affiliations
- University of Hong Kong (HK)
- University of Maryland, College Park (US)
- University of California, Berkeley (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 277-288
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1604615751
- Cited by
- 82
- References
- 15
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 2 · 2023: 4 · 2022: 16 · 2021: 15 · 2020: 12 · 2019: 7 · 2018: 9 · 2017: 3 · 2016: 4 · 2015: 1 · 2014: 1 · 2013: 1 · 2012: 1
- FWCI
- 0.96
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- differential privacy, lower bounds, coalition attacks, (ε, δ)-differential privacy, additive error
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