Dataset Information

A game theoretic framework for analyzing re-identification risk.

ABSTRACT: Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.

SUBMITTER: Wan Z

PROVIDER: S-EPMC4373733 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A game theoretic framework for analyzing re-identification risk.

Wan Zhiyu Z Vorobeychik Yevgeniy Y Xia Weiyi W Clayton Ellen Wright EW Kantarcioglu Murat M Ganta Ranjit R Heatherly Raymond R Malin Bradley A BA

PloS one 20150325 3

Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of ...[more]

PMID: 25807380

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Project description:ObjectiveElectronic medical records (EMRs) are increasingly repurposed for activities beyond clinical care, such as to support translational research and public policy analysis. To mitigate privacy risks, healthcare organizations (HCOs) aim to remove potentially identifying patient information. A substantial quantity of EMR data is in natural language form and there are concerns that automated tools for detecting identifiers are imperfect and leak information that can be exploited by ill-intentioned data recipients. Thus, HCOs have been encouraged to invest as much effort as possible to find and detect potential identifiers, but such a strategy assumes the recipients are sufficiently incentivized and capable of exploiting leaked identifiers. In practice, such an assumption may not hold true and HCOs may overinvest in de-identification technology. The goal of this study is to design a natural language de-identification framework, rooted in game theory, which enables an HCO to optimize their investments given the expected capabilities of an adversarial recipient.MethodsWe introduce a Stackelberg game to balance risk and utility in natural language de-identification. This game represents a cost-benefit model that enables an HCO with a fixed budget to minimize their investment in the de-identification process. We evaluate this model by assessing the overall payoff to the HCO and the adversary using 2100 clinical notes from Vanderbilt University Medical Center. We simulate several policy alternatives using a range of parameters, including the cost of training a de-identification model and the loss in data utility due to the removal of terms that are not identifiers. In addition, we compare policy options where, when an attacker is fined for misuse, a monetary penalty is paid to the publishing HCO as opposed to a third party (e.g., a federal regulator).ResultsOur results show that when an HCO is forced to exhaust a limited budget (set to $2000 in the study), the precision and recall of the de-identification of the HCO are 0.86 and 0.8, respectively. A game-based approach enables a more refined cost-benefit tradeoff, improving both privacy and utility for the HCO. For example, our investigation shows that it is possible for an HCO to release the data without spending all their budget on de-identification and still deter the attacker, with a precision of 0.77 and a recall of 0.61 for the de-identification. There also exist scenarios in which the model indicates an HCO should not release any data because the risk is too great. In addition, we find that the practice of paying fines back to a HCO (an artifact of suing for breach of contract), as opposed to a third party such as a federal regulator, can induce an elevated level of data sharing risk, where the HCO is incentivized to bait the attacker to elicit compensation.ConclusionsA game theoretic framework can be applied in leading HCO's to optimized decision making in natural language de-identification investments before sharing EMR data.

Dataset Information

A game theoretic framework for analyzing re-identification risk.

Publications

A game theoretic framework for analyzing re-identification risk.

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