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Natural differential privacy-a perspective on protection guarantees.

Micah Altman¹, Aloni Cohen²

¹CREOS, MIT Libraries, Massachusetts Institute of Technology, Cambridge, MA, United States.

Peerj. Computer Science

|October 9, 2023

Summary

This summary is machine-generated.

Natural differential privacy (NDP) leverages hardware for private computations, offering strong privacy loss bounds and practical privacy solutions. This approach efficiently implements privacy principles, bridging formal and substantive protections.

Keywords:

Differential privacy No free lunch Physical mechanisms Privacy by default Privacy by design

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Area of Science:

Computer Science
Information Security
Privacy Engineering

Background:

Differential privacy is a robust standard for data privacy.
Existing methods for differential privacy can be computationally intensive.
Hardware capabilities are often underutilized for privacy-preserving computations.

Purpose of the Study:

Introduce Natural Differential Privacy (NDP) as a novel approach to privacy.
Demonstrate NDP's ability to provide strong privacy guarantees.
Explore the practical implementation and benefits of NDP.

Main Methods:

Utilizing existing hardware architecture features for private computations.
Developing efficient algorithms for NDP implementation.
Analyzing privacy loss bounds and computational efficiency.

Main Results:

NDP guarantees strong bounds on privacy loss.
NDP offers a practical solution, challenging no-free-lunch theorems in privacy.
Efficient implementation strategies for NDP are presented.

Conclusions:

NDP presents a practical and efficient method for achieving differential privacy.
The approach aligns with established privacy principles and frameworks.
NDP highlights the synergy between hardware capabilities and formal privacy guarantees.