Safeguarding User Privacy in Multi-Table Synthetic Data: A New Approach to Membership Inference Attacks

The Evolving Landscape of Data Privacy and Synthetic Data

      In the age of vast digital information, the secure sharing of data is paramount, especially for enterprises dealing with sensitive customer, operational, and financial records. Synthetic tabular data has emerged as a powerful tool, offering a pathway to share information for analytics, development, and research without exposing original, sensitive datasets. This innovative approach generates artificial data that mirrors the statistical properties of real data, upholding utility while aiming to preserve privacy. Historically, much of the progress in synthetic data generation has centered on single-table applications, where each row represents a complete, independent entity.

      However, the reality of enterprise data is far more complex. Most real-world information resides in intricate relational databases, where a single user's profile or an entity's complete record is distributed across multiple interconnected tables. For example, a customer's purchasing history might be in one table, their personal details in another, and their service interactions in a third, all linked by unique identifiers. While advanced methods for generating synthetic relational data have recently surfaced, their release introduces unique privacy challenges. Information leakage can occur not just from isolated data points but critically, through the relationships that define a complete user entity. To address this sophisticated problem, a groundbreaking approach to auditing empirical user-level privacy in synthetic relational data has been proposed in the academic paper "Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting". This method highlights that traditional, single-table privacy audits often dramatically underestimate the real risk of privacy breaches. ARSA Technology, with its expertise in AI and IoT solutions, understands the critical importance of such advanced privacy auditing for modern enterprises, a capability it has honed through being experienced since 2018 in developing secure data solutions.

Beyond Single Tables: Understanding Relational Data Leakage

      A relational database is essentially a collection of interconnected tables, where each table contains rows (observations) and columns (features). The "connections" are established through primary and foreign keys. A primary key uniquely identifies a row within its table, while a foreign key in one table references a primary key in another, creating a link. This structure means that a "user" or "entity" isn't a single row but a constellation of related items spread across different tables. For instance, in a healthcare database, a patient's demographics might be in one table, their diagnoses in another, and their treatment records in a third. All these tables are interlinked to form a complete patient profile.

      The challenge with synthetic relational data arises because generative models learn not just the individual data points within each table, but also the intricate conditional dependencies that exist between these tables. When privacy is only considered at an "item-level"—meaning, auditing whether a specific row from a specific table was part of the original training data—it fails to account for the holistic nature of a user's information. If an adversary can infer that even one item or a specific relationship belonging to a user was part of the training data, it can implicitly leak sensitive information about all other connected items belonging to that user. This interconnectedness means that protecting privacy demands a "user-level" perspective, where the entire subgraph of a user's information, spanning multiple tables, is considered the unit of privacy. Failure to do so exposes organizations to significant risks of data exposure and compliance breaches.

The Flaws of Traditional Privacy Audits in Complex Data Environments

      Membership Inference Attacks (MIAs) are a class of adversarial techniques designed to determine if a specific record was part of a machine learning model's training data. These attacks exploit subtle differences in how a model behaves when presented with data it has seen versus data it hasn't. MIAs have proven invaluable for empirically auditing the privacy of single-table synthetic data generators, offering a quantifiable estimate of potential leakage, often expressed in terms of differential privacy.

      However, existing MIAs are fundamentally inadequate for thoroughly auditing multi-table synthetic data privacy at the user level. Their limitation stems from their "item-level" focus; they examine individual rows or items in isolation. In a relational database where a user's data is fragmented and linked across several tables, these attacks cannot effectively exploit the inter-tabular relationships that define a complete user profile. For instance, knowing that a specific purchase record (an "item") was in the training set doesn't necessarily reveal which customer made that purchase if the customer's identity is in a different, unlinked table. But if the attack could infer the entire customer profile (spanning multiple tables), the privacy breach is far more significant. This fundamental flaw means that traditional MIAs significantly underestimate the actual privacy risks associated with releasing multi-table synthetic datasets, leaving enterprises vulnerable to sophisticated breaches. For companies relying on synthetic data for secure analytics, this gap represents a critical unaddressed security vulnerability.

Introducing Multi-Table Membership Inference Attack (MT-MIA)

      To overcome the limitations of traditional, item-level privacy auditing, researchers have developed a novel Membership Inference Attack specifically designed for multi-table synthetic data: the Multi-Table Membership Inference Attack (MT-MIA). This innovative approach fundamentally shifts the auditing focus from individual data points to the complete "user entity," which is represented as a "subgraph" encompassing all interconnected information across various tables.

      MT-MIA tackles this by first conceptualizing relational databases as heterogeneous graphs. In this context, different tables become different types of "nodes" (e.g., customer nodes, transaction nodes, product nodes), and the join relationships between them become different types of "edges." The attack then employs Heterogeneous Graph Neural Networks (HGNNs) to learn graphical representations of these user-centric subgraphs. HGNNs are a type of AI algorithm particularly adept at processing and understanding data structured as graphs, especially when those graphs contain different types of nodes and edges, perfectly suited for relational databases. Under a No-Box threat model, the adversary only has access to the synthetic data and does not know the internal workings of the synthetic data generator. This realistic scenario makes MT-MIA a robust and practical tool for assessing privacy. Unlike previous attacks, MT-MIA explicitly leverages all relational information connected to a user, directly targeting vulnerabilities induced by the inter-tabular conditional dependencies that are invisible to single-table methodologies. This model-agnostic approach can be applied across various multi-table datasets and synthetic data generators, providing a versatile tool for privacy practitioners and researchers. Such advanced analytical capabilities are increasingly crucial for integrating complex data sources, aligning with the functionalities offered by platforms like the ARSA AI API, which provides modular AI services for complex data scenarios.

Practical Implications and Empirical Findings

      The empirical validation of MT-MIA has yielded compelling results, underscoring the critical need for user-level privacy auditing. In scenarios specifically designed to highlight egregious privacy leakage in multi-table settings, traditional single-table, item-level attacks performed no better than random guessing when attempting to infer user-level membership. This stark finding confirms their inadequacy. In contrast, MT-MIA achieved near-perfect accuracy (AUC close to 1.0), effectively demonstrating its ability to detect these sophisticated user-level privacy breaches.

      This significant outcome confirms that a distinct vulnerability exists within current state-of-the-art multi-table synthetic data generators. Even when operating under conservative threat models, these generators are susceptible to leaking user-level information through the interconnections of data. Beyond merely detecting leakage, MT-MIA offers a diagnostic capability. By analyzing its intermediate embedding spaces, the attack can pinpoint where memorization of original training data might be occurring within the synthetic dataset. This allows developers of synthetic data generators to identify and address specific weaknesses in their models, enhancing overall privacy protection. This type of deep analytical insight into data patterns and anomalies is a core strength of advanced AI Video Analytics, which ARSA Technology applies to extract actionable intelligence from complex data streams for security and operational optimization.

Securing the Future of Synthetic Data Sharing

      The rise of multi-table synthetic data is undeniably a positive development, promising to unlock new opportunities for data sharing and innovation while upholding privacy principles. However, as this research demonstrates, the complexity of relational databases introduces nuanced privacy challenges that traditional auditing methods cannot adequately address. The proposed Multi-Table Membership Inference Attack (MT-MIA) serves as a critical advancement, providing a robust, user-level mechanism to audit and diagnose privacy vulnerabilities that were previously underestimated.

      For enterprises leveraging synthetic data, understanding these advanced attack vectors is crucial for maintaining data security, ensuring regulatory compliance (like GDPR/PDPA), and building trust with customers. Proactive privacy auditing is no longer a luxury but a necessity for any organization committed to responsible data stewardship. Developers of synthetic data generators must integrate such comprehensive privacy auditing into their development lifecycle to ensure the robustness of their offerings. Solutions that prioritize privacy-by-design and enable secure, on-premise processing of sensitive data, such as ARSA Technology's AI Box Series, are essential components in this evolving landscape, offering a foundation for robust and trustworthy AI applications.

      To learn more about advanced AI solutions and how to enhance your data security and operational intelligence, we invite you to explore ARSA Technology's comprehensive offerings and contact ARSA for a free consultation.

      Source: Ward, J., Wang, C-H., & Cheng, G. (2026). Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting. arXiv preprint arXiv:2602.07126.