To Warn or Not to Warn: Online Signaling in Audit Games

Routine operational use of sensitive data is often governed by law and regulation. For instance, in the medical domain, there are various statues at the state and federal level that dictate who is permitted to work with patients' records and under what conditions. To screen for potential privacy breaches, logging systems are usually deployed to trigger alerts whenever a suspicious access is detected. However, such mechanisms are often inefficient because 1) the vast majority of triggered alerts are false positives, 2) small budgets make it unlikely that a real attack will be detected, and 3) attackers can behave strategically, such that traditional auditing mechanisms cannot easily catch them. To improve efficiency, information systems may invoke signaling, so that whenever a suspicious access request occurs, the system can, in real time, warn the user that the access may be audited. Then, at the close of a finite period, a selected subset of suspicious accesses are audited. This gives rise to an online problem in which one needs to determine 1) whether a warning should be triggered and 2) the likelihood that the data request event will be audited. In this paper, we formalize this auditing problem as a Signaling Audit Game (SAG), in which we model the interactions between an auditor and an attacker in the context of signaling and the usability cost is represented as a factor of the auditor's payoff. We study the properties of its Stackelberg equilibria and develop a scalable approach to compute its solution. We show that a strategic presentation of warnings adds value in that SAGs realize significantly higher utility for the auditor than systems without signaling. We perform a series of experiments with 10 million real access events, containing over 26K alerts, from a large academic medical center to illustrate the value of the proposed auditing model and the consistency of its advantages over existing baseline methods.

I. Introduction

Our society now collects, stores, and processes personal and intimate data with ever-finer detail, documenting our activities and innovations in a wide range of domains, ranging from health to finance [1], [2]. Due to the potential value of such data, their management systems face non-trivial challenges to personal privacy and organizational secrecy. The sensitive nature of the data stored in such systems attracts malicious attackers who can gain value by disrupting them in various ways (e.g., stealing sensitive information, commandeering computational resources, committing financial fraud, and simply shutting the system down) [3], [4]. Reports in the popular media indicate that the severity and frequency of attack events continues to grow. Notably, the recent breach at Equifax led to the exposure of data on 143 million Americans, including credit card numbers, Social Security numbers, and other information that could be used for identity theft or other illicit purposes [5]. Even more of a concern is that the exploit of the system continued for at least two months before it was discovered.