The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.
Technical Privacy Metrics: A Systematic Survey
Abstract
The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature make an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey, we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over 80 privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement.
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot: An International Journal · doi:10.1108/ir.1999.04926fae.001 · Cited by 38,662
Communication Theory of Secrecy Systems
Shannon, C. E. (1949) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1949.tb00928.x · Cited by 9,440 · cited 1×
“Shannon’s criterion for perfect secrecy [114] demands that the adversary’s prior probability for the secret x∗ equals the posterior probability that takes into account a new observationy, i.”
Backgroundk-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557
Dwork, C. (2006) · Lecture Notes in Computer Science · doi:10.1007/11787006_1 · Cited by 5,291
Tor: The Second-Generation Onion Router
Dingledine, R., Mathewson, N., & Syverson, P. (2004) · doi:10.21236/ada465464 · Cited by 4,104
L -diversity: Privacy beyond k -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617
Robust De-anonymization of Large Sparse Datasets
Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372
Cited by
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407
Yin, X., Zhu, Y., & Hu, J. (2022) · ACM Computing Surveys · doi:10.1145/3460427 · Cited by 638
Preserving Data Privacy via Federated Learning: Challenges and Solutions
Li, Z., Sharma, V., & P. Mohanty, S. (2020) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2019.2959108 · Cited by 226
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Zhu, T., Ye, D., Wang, W., Zhou, W., & Yu, P. S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3014246 · Cited by 219
A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle
Shahriar, S., Allana, S., Hazratifard, S. M., & Dara, R. (2023) · IEEE Access · doi:10.1109/access.2023.3287195 · Cited by 114
Metadata
- Authors
- Wagner, I., & Eckhoff, D.
- Venue
- ACM Computing Surveys
- Published
- 2018-06-12
- Volume
- 51
- Issue
- 3
- Pages
- 1-38
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4288345394
- Cited by
- 346
- References
- 100
- FWCI
- 18.67
- Type
- journal article
- Language
- en
- Open access
- closed
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