Collecting Telemetry Data Privately (1712.01524v1)

Abstract: The collection and analysis of telemetry data from users' devices is routinely performed by many software companies. Telemetry collection leads to improved user experience but poses significant risks to users' privacy. Locally differentially private (LDP) algorithms have recently emerged as the main tool that allows data collectors to estimate various population statistics, while preserving privacy. The guarantees provided by such algorithms are typically very strong for a single round of telemetry collection, but degrade rapidly when telemetry is collected regularly. In particular, existing LDP algorithms are not suitable for repeated collection of counter data such as daily app usage statistics. In this paper, we develop new LDP mechanisms geared towards repeated collection of counter data, with formal privacy guarantees even after being executed for an arbitrarily long period of time. For two basic analytical tasks, mean estimation and histogram estimation, our LDP mechanisms for repeated data collection provide estimates with comparable or even the same accuracy as existing single-round LDP collection mechanisms. We conduct empirical evaluation on real-world counter datasets to verify our theoretical results. Our mechanisms have been deployed by Microsoft to collect telemetry across millions of devices.

• The paper introduces a 1-bit mechanism for mean estimation under local differential privacy, achieving competitive accuracy with streamlined implementation.
• The paper proposes an α-point rounding technique for effective memoization, discretizing values while preserving data utility.
• The paper establishes formal privacy guarantees for continuous data collection, validated by its successful deployment in large-scale telemetry systems.

Essay: Collecting Telemetry Data Privately with Locally Differentially Private Mechanisms

The paper "Collecting Telemetry Data Privately" by Ding, Kulkarni, and Yekhanin addresses the challenge of collecting telemetry data from user devices while ensuring strong privacy guarantees. The research focuses on developing locally differentially private (LDP) mechanisms tailored for repeated collection of counter data, such as app usage statistics, without sacrificing accuracy over time. The mechanisms presented are particularly relevant in the context of tightening privacy regulations and user expectations.

Key Contributions

1. 1-Bit Mechanism for Mean Estimation: The paper introduces a simple $1$-bit response mechanism within the LDP framework for the collection of counter data aimed at tasks like mean estimation. This mechanism achieves comparable accuracy to traditional single-round LDP methods while enhancing practicality through simpler descriptions and implementations. The authors provide empirical results demonstrating performance gains in specific settings.
2. $\alpha$-Point Rounding Technique: A novel rounding technique, $\alpha$-point rounding, is proposed to enable more effective memoization. This technique discretizes users' private values, preserving their expectation and enabling the application of memoization in contexts where values are expected to change frequently.
3. Formal Privacy Guarantees: The paper rigorously defines privacy guarantees for continuous data collection scenarios. This is an extension of traditional LDP mechanisms to avoid privacy degradation over repeated collections. The approach ensures users who exhibit stable or slowly varying behavior maintain strong privacy assurances.
4. Deployment and Practical Implications: These mechanisms have been deployed in Microsoft's telemetry collection processes, protecting user privacy across millions of devices. The deployment highlights the practical applicability and scalability of the proposed LDP mechanisms.

Technical Insights

The paper discusses the inherent challenges in maintaining privacy over repeated telemetry data collections. Traditional LDP mechanisms provide robust privacy guarantees in single data collection rounds, but their effectiveness diminishes when applied repeatedly, especially for counter data subject to frequent changes.

The introduction of mechanisms such as $\alpha$-point rounding effectively addresses these challenges by applying a randomized rounding approach that maintains utility while enabling memoization to limit privacy leakage. Theoretical analysis is complemented with empirical evaluations, attesting to the mechanisms' accuracy in realistic data-collection scenarios.

Implications and Future Directions

The findings underscore the feasibility of providing robust privacy guarantees in continual telemetry data collection settings, making significant contributions to the practical implementation of LDP in large-scale systems. Beyond the current deployment, these methodological advancements can influence future developments in privacy-preserving data collection, particularly in expanding contexts like IoT and pervasive computing.

Potential future research directions include refining these mechanisms to minimize computational overhead further and extend applicability to other types of data beyond counters, such as categorical or complex data structures. Exploration of the integration of these methods with advanced machine learning models to enhance privacy-preserving analytics represents another promising avenue.

Overall, this work provides substantial evidence for the viability of LDP mechanisms in maintaining privacy over repeated data collections, demonstrating both theoretical innovation and practical applicability in real-world deployments.