Audit mechanisms for provable risk management and accountable data governance

• Main Authors: BLOCKI, Jeremiah, CHRISTIN, Nicolas, DATTA, Anupam, SINHA, Arunesh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Expected Utility; Repeated Game; Subgame Perfect Equilibrium; Public Signal; Inside Attack; Databases and Information Systems; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/4490; https://ink.library.smu.edu.sg/context/sis_research/article/5493/viewcontent/GameSec12_main_1_.pdf
• Institution: Singapore Management University

Organizations that collect and use large volumes of personal information are expected under the principle of accountable data governance to take measures to protect data subjects from risks that arise from inapproriate uses of this information. In this paper, we focus on a specific class of mechanisms—audits to identify policy violators coupled with punishments—that organizations such as hospitals, financial institutions, and Web services companies may adopt to protect data subjects from privacy and security risks stemming from inappropriate information use by insiders. We model the interaction between the organization (defender) and an insider (adversary) during the audit process as a repeated game. We then present an audit strategy for the defender. The strategy requires the defender to commit to its action and when paired with the adversary’s best response to it, provably yields an asymmetric subgame perfect equilibrium. We then present two mechanisms for allocating the total audit budget for inspections across all games the organization plays with different insiders. The first mechanism allocates budget to maximize the utility of the organization. Observing that this mechanism protects the organization’s interests but may not protect data subjects, we introduce an accountable data governance property, which requires the organization to conduct thorough audits and impose punishments on violators. The second mechanism we present achieves this property. We provide evidence that a number of parameters in the game model can be estimated from prior empirical studies and suggest specific studies that can help estimate other parameters. Finally, we use our model to predict observed practices in industry (e.g., differences in punishment rates of doctors and nurses for the same violation) and the effectiveness of policy interventions (e.g., data breach notification laws and government audits) in encouraging organizations to adopt accountable data governance practices.