Differential Privacy Techniques for Cyber Physical Systems
K-Anonymity is still satisfied with better Data Utility Adversary cannot identify Alice or her disease from the released record However, k-Anonymity still has several shortcomings Example of k-Anonymity 12 Quasi-Identifier Sensitive Attribute Gender Age Zip Code Disease * 2* [5206*, 5207*] Breast Cancer * 2* [5206*, 5207*] Breast Cancer Abstract: A common view in some data anonymization literature is to oppose the "old'' k-anonymity model to the "new'' differential privacy model, which offers more robust privacy guarantees. However, the utility of the masked results provided by differential privacy is usually limited, due to the amount of noise that needs to be added to the k-anonymity and -differential privacy and is validated in an extensive experimental campaign using openly available datasets. Index T erms —Differential privacy, k-anonymity , data privacy , The focus of this paper is to propose a privacy-preserving approach called (K, L) Anonymity that combines k-anonymity and Laplace differential privacy techniques. This coherent model guarantees privacy from linkage attacks as the risk is mitigated through experimental results. While both differential privacy and k-anonymity models have been extensively studied for sensitive data publication [13], [14], [48], [52], [38], recent work has developed solutions to support
Duet is a programming language which automatically derives (and checks) differential privacy bounds for programs written in the language. Duet is designed to support modern machine learning algorithms, and advanced variants of differential privacy in order to add minimal noise to algorithm results in order to ensure privacy. Notes: paper
Enhancing data utility in differential privacy via It is not uncommon in the data anonymization literature to oppose the “old” \(k\)-anonymity model to the “new” differential privacy model, which offers more robust privacy guarantees.Yet, it is often disregarded that the utility of the anonymized results provided by differential privacy is quite limited, due to the amount of noise that needs to be added to the output, or because
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Differential privacy: its technological prescriptive using Differential privacy [] is a technology that provides researchers and database analysts a facility to obtain the useful information from the databases that contain personal information of people without revealing the personal identities of the individuals.This is done by introducing a minimum distraction in the information provided by the database system. What is Differential Privacy, and why is it needed? - LeapYear Feb 04, 2020