# AuditShare: Sensitive Data Sharing with Reliable Leaker Identification

## TL;DR

AuditShare is a system designed for sharing PII data among multiple parties that reliably identifies the source of leaks, even under collusion and attack scenarios, using data allocation, immutable records, and a knowledge-based algorithm.

## Contribution

It introduces a novel PII sharing system that ensures reliable leak source identification through data allocation, Merkle Tree records, and a knowledge-based detection algorithm.

## Key findings

- Achieves over 99.99% accuracy in leak source identification.
- Can share 100,000 data objects in 0.5 seconds.
- Effectively identifies guilty parties even with modest leaked data.

## Abstract

As Personally Identifiable Information (PII) data sharing among multiple parties becomes increasingly common, so does the potential for data leakage. As required by new data protection regulations and laws, when PII leakage occurs, one must be able to reliably identify the leaking sources. Existing solutions utilize watermark technologies or data object allocation strategies to differentiate the data shared with different parties to identify potential leakers. However, these solutions lose their effectiveness under several attack scenarios, e.g., a data sender may leak the data and a receiver may deny the reception of certain shared data. Worse yet, multiple receivers might collude and apply a set of operations such as intersection, complement, and union to their received datasets before leaking them, making the task of leaker identification even more difficult. In this paper, we propose AuditShare, a PII dataset sharing system with reliable leaking source identification. Firstly, taking advantage of the intrinsic properties of PII data, AuditShare allocates data objects to individual sharing parties by PII attributes. Secondly, AuditShare obliviously transfers data between the sender and each receiver and uses a Merkle Tree as an immutable record of the sharing. Thirdly, a knowledge-based identification algorithm is proposed to identify a guilty sender or colluding/non-colluding receivers. Through our evaluation, we show that: (i) With a modest amount of leaked data, AuditShare can accurately (accuracy>99.99%) and undeniably identify all the guilty parties in different cases; (ii) It only takes 0.5 second to share 100,000 data objects in AuditShare, which is practical in real-world deployment.