Modeling and Analysis of Information Leakage Under Multiple Adversaries

The rapid growth of IoT devices and applications has resulted in the generation and transmission of large volumes of private data, which encompass various dimensions and are observable by multiple entities. To address the need for balancing confidentiality and data utility, the Quantitative Information Flow (QIF) approach has been developed to quantify information leakage in computer systems. However, it assumes a single adversary seeking to infer a single piece of secret information. This paper examines leakage measures in the Multiple Attributes and Adversaries (MAA) scenario where attributes are under coordinated attacks from multiple adversaries through individual side channels. The paper reveals that the current model, when applied individually to each attribute or adversary, only considers scenarios where all attributes are leaked to all adversaries. However, any leakage of an attribute to any adversary should be deemed a breach of private data. To tackle this, the paper introduces the MAA leakage model and validates its compliance with a leakage measure and operational interpretation of the MAA threat model. Additionally, it presents a closed-form expression for the upper bound of MAA leakage within a given set of side channels, demonstrating that the upper bound is achieved when all attributes conform to a uniform distribution.