Protection Against Reconstruction and Its Applications in Private Federated Learning (1812.00984v2)

Abstract: In large-scale statistical learning, data collection and model fitting are moving increasingly toward peripheral devices---phones, watches, fitness trackers---away from centralized data collection. Concomitant with this rise in decentralized data are increasing challenges of maintaining privacy while allowing enough information to fit accurate, useful statistical models. This motivates local notions of privacy---most significantly, local differential privacy, which provides strong protections against sensitive data disclosures---where data is obfuscated before a statistician or learner can even observe it, providing strong protections to individuals' data. Yet local privacy as traditionally employed may prove too stringent for practical use, especially in modern high-dimensional statistical and machine learning problems. Consequently, we revisit the types of disclosures and adversaries against which we provide protections, considering adversaries with limited prior information and ensuring that with high probability, ensuring they cannot reconstruct an individual's data within useful tolerances. By reconceptualizing these protections, we allow more useful data release---large privacy parameters in local differential privacy---and we design new (minimax) optimal locally differentially private mechanisms for statistical learning problems for \emph{all} privacy levels. We thus present practicable approaches to large-scale locally private model training that were previously impossible, showing theoretically and empirically that we can fit large-scale image classification and LLMs with little degradation in utility.

• The paper redefines local privacy by relaxing rigid constraints to better balance user data protection and model accuracy.
• It introduces novel locally differentially private mechanisms that achieve minimax-optimal convergence for high-dimensional federated learning.
• The research offers a practical framework for designing federated learning systems that safeguard against data reconstruction in real-world applications.

Protection Against Reconstruction and Its Applications in Private Federated Learning

The paper "Protection Against Reconstruction and Its Applications in Private Federated Learning" focuses on the challenges of maintaining privacy in decentralized statistical learning, particularly in federated learning on peripheral devices like phones and fitness trackers. The authors propose a framework for balancing the trade-off between data privacy and the utility of statistical models derived from such distributed data sources.

Key Contributions

1. Revisiting Local Privacy: The authors critically analyze traditional notions of local differential privacy (LDP), suggesting it might be excessively rigid for high-dimensional data. They propose reconceptualizing adversaries' capabilities and types of disclosures, allowing the relaxing of privacy constraints while maintaining high privacy protection levels.
2. Protection Against Reconstruction: The paper defines a novel privacy paradigm that focuses on protecting against the reconstruction of an individual's data within useful tolerances. This approach is more aligned with real-world scenarios where adversaries have limited prior knowledge.
3. Mechanisms for Private Learning: A set of new locally differentially private mechanisms are developed, particularly for high-dimensional data, that balance the need for privacy against the degradation of data utility. The authors demonstrate these mechanisms are minimax-optimal across a range of privacy levels.
4. Federated Learning System Design: The paper presents a system design for large-scale, private federated learning that maintains user privacy against both internal onlookers and potential external adversaries.

Detailed Analysis

• Local versus Centralized Privacy: The paper distinguishes between locally private algorithms, where data is privatized at the source, and centralized differential privacy, which assumes a level of trust with a data curator. The authors explore the limitations of both models and propose mechanisms to mitigate these by ensuring that adversaries cannot reconstruct accurate data from privatized outputs.
• Mechanism Design: The authors introduce mechanisms based on separation of vector direction and magnitude, optimizing them for different privacy levels. Specifically, these mechanisms address the significant challenges encountered with high-dimensional data where standard LDP approaches compromise utility.
• Asymptotic Analysis: The paper delivers a comprehensive asymptotic analysis of their proposed learning procedures, proving that the new privacy mechanisms achieve optimal convergence rates. This analysis underscores the practicality and efficiency of their methods for real-world applications.

Implications and Future Directions

1. Practicality in Applications: The proposed privacy mechanisms provide viable solutions for deploying privacy-preserving models in applications like image classification and language processing on edge devices without sacrificing too much on performance.
2. Improved Privacy Constructs: By focusing on protection against data reconstruction, the paper moves towards a more adaptable privacy model that can be fine-tuned for specific application needs, potentially influencing future privacy regulation and compliance standards.
3. Future Research Directions: Further studies could involve adapting these privacy mechanisms to other learning paradigms within artificial intelligence and exploring their impact on emerging decentralized learning frameworks.

Overall, this research articulates a nuanced perspective on privacy in distributed statistical learning and federated learning, presenting sophisticated methods to protect users' data while enabling robust model performance. With the continued proliferation of edge devices and IoT technologies, such advances are crucial for privacy-preserving data utilization.