Instance-Optimal Clipping for Summation Problems in Shuffle Model of Differential Privacy

מושגי ליבה

Clipping mechanism optimizes error bounds in shuffle-DP for sum estimation problems.

תקציר

The article discusses the instance-specific error bounds achievable through the clipping mechanism in the shuffle model of differential privacy. It introduces a protocol that partitions the domain to estimate sums efficiently, achieving optimal error rates without splitting privacy budgets. The protocol ensures (ε, δ)-DP and minimizes communication complexity while maintaining accuracy.
The authors propose a one-round solution for high-dimensional sum estimation, improving upon existing two-round protocols. By leveraging domain partitioning and parallel composition, they achieve optimal error bounds with practical implementation.
Overall, the article presents innovative approaches to enhance privacy-preserving sum estimation under differential privacy constraints.

סטטיסטיקה

O(U/ε)
O(maxi xi·log log U/ε)
O(1/ε)
O(√n/ε)
O(Max(D) · log log U/ε)
˜O(Max(D) · log3.5 U p log(1/δ)/ε)

ציטוטים

"The clipping mechanism can achieve an instance-specific error bound depending on Max(D)."
"Domain partitioning lowers the total message complexity of all BaseSumDP instances."
"Our final protocol achieves optimal error rates without splitting privacy budgets."

תובנות מפתח מזוקקות מ:

Instance-optimal Clipping for Summation Problems in the Shuffle Model of Differential Privacy

by Wei Dong,Qiy... ב- arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10116.pdf

שאלות מעמיקות

How does the proposed protocol compare to existing two-round solutions in terms of practicality and efficiency

The proposed one-round protocol for high-dimensional sum estimation outperforms existing two-round solutions in terms of practicality and efficiency. The key advantage lies in the reduction of communication complexity and latency associated with two-round protocols. By incorporating domain partitioning and parallel composition, the protocol achieves optimal error bounds while maintaining a single round of communication per user. This streamlined approach eliminates the need for multiple rounds of interaction, reducing coordination overhead and information leakage.

What are the potential implications of implementing this one-round protocol for real-world applications beyond sum estimation

Implementing this one-round protocol for high-dimensional sum estimation can have significant implications for real-world applications beyond sum estimation. The efficient handling of privacy-preserving computations in distributed settings opens up avenues for secure collaborative data analysis across diverse domains such as healthcare, finance, and telecommunications. By enabling accurate aggregation of sensitive information while preserving individual privacy, the protocol facilitates seamless collaboration among multiple parties without compromising data confidentiality.

How might advancements in privacy technology like this impact data analytics and machine learning practices

Advancements in privacy technology like the proposed one-round protocol have far-reaching implications for data analytics and machine learning practices. Firstly, it enhances the scalability and efficiency of privacy-preserving algorithms in distributed environments, enabling organizations to leverage large datasets without sacrificing data security. Secondly, it promotes trust among stakeholders by ensuring robust protection against unauthorized access to sensitive information. Lastly, it paves the way for innovative research opportunities in areas such as federated learning, secure multi-party computation, and differential privacy-enhanced machine learning models.