Differentially Private Synthesis of Databases for Benchmark Publishing

Core Concepts

PrivBench, a framework that leverages sum-product networks (SPNs) with differential privacy, can synthesize high-quality databases that maintain privacy while closely resembling the original data in terms of data distribution and query performance.

Abstract

The paper presents PrivBench, a framework for privacy-preserving database synthesis, aimed at addressing the challenge of balancing privacy preservation and data fidelity for benchmarking purposes.

Key highlights:

• Existing benchmarks often fail to reflect the varied nature of user workloads, leading to the need for more tailored databases that incorporate real-world user data.
• However, privacy concerns deter users from directly sharing their data, underscoring the importance of creating synthesized databases that prioritize privacy protection.
• PrivBench leverages sum-product networks (SPNs) to model the data distribution and dependencies within the input database, while incorporating differential privacy (DP) to ensure privacy preservation.
• The framework consists of three main steps:
  1. Private SPN Construction: Constructing differentially private SPNs for each table in the input database.
  2. Private Fanout Construction: Complementing the SPNs with differentially private fanout distributions to capture primary-foreign key references.
  3. SPN-Based Database Synthesis: Sampling synthetic data from the modified SPNs to generate the final differentially private database.
• PrivBench is designed to closely match the original database in terms of data distribution and query performance, as measured by metrics like query execution time error and Q-error on query cardinalities.
• Experimental results demonstrate that PrivBench outperforms existing privacy-preserving data publishing methods and non-private data generation approaches in terms of these benchmarking-related metrics.

Stats

The paper does not provide any specific numerical data or statistics. The focus is on the methodology and evaluation of the proposed PrivBench framework.

Quotes

"Benchmarking is crucial for evaluating a DBMS, yet existing benchmarks often fail to reflect the varied nature of user workloads."
"Differential privacy has become a key method for safeguarding privacy when sharing data, but the focus has largely been on minimizing errors in aggregate queries or classification tasks, with less attention given to benchmarking factors like runtime performance."

Key Insights Distilled From

Privacy-Enhanced Database Synthesis for Benchmark Publishing

by Yongrui Zhon... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01312.pdf

Deeper Inquiries

How can PrivBench be extended to handle more complex database schemas, such as those with multiple foreign key relationships

To extend PrivBench to handle more complex database schemas with multiple foreign key relationships, we can modify the Private Fanout Construction step. Currently, PrivBench complements SPNs with leaf nodes storing fanout distributions for primary-foreign key references. We can enhance this process by incorporating a mechanism to handle multiple foreign key relationships. This can be achieved by iterating through all foreign keys in the referencing table and creating fanout tables for each foreign key. The fanout distributions for each foreign key can then be perturbed and stored in new leaf nodes, similar to the current process for single foreign key relationships. By extending this functionality, PrivBench can effectively model and preserve the complex relationships present in databases with multiple foreign keys.

What are the potential limitations of using SPNs for modeling data distributions, and how could alternative data modeling techniques be incorporated into PrivBench

While SPNs are effective in modeling data distributions, they may have limitations when dealing with extremely large datasets or highly complex data structures. One potential limitation is the scalability of SPNs, as constructing and manipulating large SPNs can be computationally intensive. To address this, alternative data modeling techniques such as deep learning models like neural networks or probabilistic graphical models could be incorporated into PrivBench. These models can offer more flexibility and scalability in handling complex data distributions and relationships. By integrating these alternative techniques alongside SPNs, PrivBench can enhance its capability to accurately model diverse data structures and distributions.

Given the focus on benchmarking, how could PrivBench be adapted to generate synthetic data for testing specific DBMS features or workloads, beyond just overall performance

To adapt PrivBench for generating synthetic data tailored to specific DBMS features or workloads, we can introduce customization options in the synthesis framework. This customization could involve incorporating parameters or settings that allow users to specify the characteristics they want to focus on during data generation. For testing specific DBMS features, users could define constraints or patterns that need to be present in the synthetic data. Additionally, for testing specific workloads, users could input queries or query patterns that the synthetic data should be optimized for. By allowing users to tailor the synthesis process based on their specific requirements, PrivBench can generate synthetic data that closely aligns with the intended testing scenarios, beyond just overall performance benchmarking.