K-stars LDP: A Novel Framework for (p, q)-clique Enumeration under Local Differential Privacy

Keskeiset käsitteet

The author proposes a novel k-stars LDP algorithm for (p, q)-clique enumeration with improved utility and privacy protection.

Tiivistelmä

The K-stars LDP algorithm introduces a new approach to protect user privacy while counting subgraphs. It outperforms traditional edge LDP algorithms by reducing noise and improving accuracy. The theoretical analysis and experiments demonstrate the effectiveness of the k-stars LDP algorithm in various datasets.

Tilastot

The Gplus dataset has 107,614 nodes and 12,238,285 edges.
The IMDB dataset has 896,308 nodes and 57,064,385 edges.
The GitHub dataset has 177,386 nodes and 440,237 edges.
The Facebook dataset has 63,732 nodes and 1,545,686 edges.

Lainaukset

"Our proposed k-stars LDP algorithm has a better utility than traditional edge LDP algorithm."
"The K-stars LDP algorithm utilizes the structure information within (p, q)-cliques to reduce noise and improve performance."

Tärkeimmät oivallukset

K-stars LDP

by Henan Sun,Zh... klo arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01788.pdf

Syvällisempiä Kysymyksiä

How does the sparsity of graphs affect the performance of the k-stars LDP algorithm

The sparsity of graphs plays a significant role in influencing the performance of the k-stars LDP algorithm. In sparse graphs, where there are fewer edges compared to nodes, the number of subgraphs like (2, 2)-cliques may be lower. This results in a different distribution and density of subgraphs within the graph structure. The k-stars LDP algorithm benefits from this sparsity by requiring less noise to obfuscate the existence or non-existence of k-stars compared to traditional edge LDP algorithms. With fewer edges and subgraphs to consider, the algorithm can achieve better utility and accuracy in sparse graph scenarios.

What are the implications of using k-stars as obfuscated units for privacy protection in other applications

Using k-stars as obfuscated units for privacy protection has implications beyond graph theory applications. The concept of k-stars provides a more efficient way to protect user privacy while analyzing complex structures such as subgraphs like (p, q)-cliques. By considering k-stars as basic obfuscated units similar to edges but with reduced noise requirements, it allows for enhanced data utility without compromising on privacy protection. This approach can be applied in various domains where sensitive information needs to be analyzed while maintaining individual privacy.
In other applications such as healthcare data analysis or financial transactions monitoring, utilizing k-stars for privacy protection could offer advantages in terms of preserving data confidentiality while still allowing for meaningful insights to be derived from the data. By leveraging the structure information within these datasets through k-stars LDP techniques, organizations can ensure compliance with privacy regulations without sacrificing analytical capabilities.

How can the concept of k-stars be applied to enhance privacy in different types of data analysis beyond graph theory

The concept of using k-stars for enhancing privacy can be extended beyond graph theory into different types of data analysis scenarios:

Healthcare Data Analysis: In medical research or patient records analysis, identifying patterns within medical conditions or treatment outcomes while protecting patient confidentiality is crucial. Applying k-stars LDP techniques could enable researchers to analyze relationships between symptoms or treatments without exposing individual patient details.

Financial Data Security: When analyzing financial transactions or market trends, ensuring customer anonymity is essential for regulatory compliance and trust-building purposes. Utilizing k-star obfuscation methods could allow financial institutions to detect fraudulent activities or monitor market behaviors while safeguarding sensitive customer information.

Marketing Analytics: In marketing analytics where consumer behavior patterns are studied based on demographic information or purchase history, incorporating k-star privacy measures could help companies derive valuable insights without compromising customer identities.

By integrating the concept of using k-stars as obfuscated units into various data analysis frameworks outside graph theory contexts, organizations can strike a balance between extracting meaningful insights from sensitive datasets and upholding individuals' right to data privacy.