Privacy, Accuracy, and Consistency: A Data Analysis of the Netflix Prize Dataset
đ Abstract
We present a new class of privacy attacks against high-dimensional micro-data, based on the observation that large-scale data analyses often produce aggregate results that are available to the adversary. As a case study, we demonstrate our attacks on the Netflix Prize dataset, which contains anonymous movie ratings for over 500,000 subscribers, released as part of an open competition to improve Netflixâs movie recommendation algorithm. We show that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriberâs record if it is present in the dataset. Our results highlight the risks of anonymized data, demonstrating that even heavily âanonymizedâ datasets are not immune to privacy attacks.
⨠Summary
The paper by Arvind Narayanan and Vitaly Shmatikov titled âPrivacy, Accuracy, and Consistency: A Data Analysis of the Netflix Prize Datasetâ was presented in 2007 and explores privacy risks associated with anonymized datasets. The authors introduce a new class of privacy attacks highlighting vulnerabilities in the Netflix Prize dataset. They show that even datasets that have undergone anonymization processes can be subject to privacy attacks if an adversary has partial information about an individual in the dataset. This work emphasizes the importance of re-evaluating anonymization techniques for maintaining data privacy, specifically showing that k-anonymity does not guarantee privacy.
The influence of this paper has been recognized widely within the field of privacy research, particularly in demonstrating the limitations of anonymization techniques. It has been referenced in subsequent research examining the challenges of data anonymization and re-identification, such as the research by Dwork in differential privacy. Furthermore, the paper has also had implications in policy making for privacy protection standards where data anonymization issues are of concern. Notable references include: - Dwork, C. (2009). âThe Differential Privacy Frontierâ. Proceedings of the IEEE Symposium on Foundations of Computer Science. Link - Ohm, P. (2010). âBroken Promises of Privacy: Responding to the Surprising Failure of Anonymizationâ. UCLA Law Review. Link
Although further referencing papers and citations are available, this summary provides a depiction of the paperâs impact and its implications for future research in privacy and data security.