Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response

핵심 개념

The paper derives the tightest known differential privacy guarantee for the shuffle model with k-Randomized Response (k-RR) local randomizers, under histogram queries, and evaluates the utility of the privacy mechanism.

초록

The paper focuses on the shuffle model (SM) of differential privacy, which is an intermediate framework between the central and local models. In the shuffle model, data owners send their locally privatized data to a server where messages are shuffled randomly, making it impossible to trace the link between a privatized message and the corresponding sender.
The key contributions of the paper are:

Deriving an analytical form of the tight differential privacy guarantee for the shuffle model with k-RR local randomizer under histogram queries. This shows that the shuffle model provides a higher level of differential privacy guarantee than what is known in the existing literature, for the same level of locally injected noise to the data.

Comparing the privacy-utility trade-off of the shuffle model and the optimized Gaussian mechanism for the histogram queries, and showing that their performances are comparable.

The paper first provides the necessary background on differential privacy, adaptive differential privacy, and the shuffle model. It then derives the tight (ε, δ)-ADP guarantee for the shuffle model with k-RR local randomizer, using the concept of adaptive differential privacy proposed by Sommer et al.
Next, the paper compares the utility of the shuffle model and the central model (with Gaussian mechanism) under the same level of differential privacy protection. Experiments are performed on both synthetic and real (Gowalla) data. The results show that the utilities of the two models are almost comparable, with the central model performing slightly better. However, the gap is very small, indicating that the shuffle model can achieve comparable utility to the central model under the same level of privacy protection.

통계

The paper does not provide any direct numerical data or statistics. However, it presents the following key figures:

Table 1 shows the values of δ obtained from the results in prior work and the proposed derivation in the paper, by varying ε from 0.1 to 0.4, fixing n = 100 and ε0 = 0.5. The proposed result gives significantly lower values of δ compared to the existing work, highlighting the tighter DP guarantee provided by the shuffle model.

Table 2 shows the default values of the parameters used for the experiments on synthetic data.

Tables 3 and 4 present the experimental results on the tight δ for different settings of ε, ε0, n and k, as well as the individual-specific utility comparison between the central and shuffle models.

Figures 2 and 3 show the experimental results on real Gowalla dataset, comparing the utility of the central model and the shuffle model under the same level of differential privacy.

인용구

The paper does not contain any direct quotes.

핵심 통찰 요약

Tight Differential Privacy Guarantees for the Shuffle Model with $k$-Randomized Response

by Sayan Biswas... 게시일 arxiv.org 05-01-2024

https://arxiv.org/pdf/2205.08858.pdf

더 깊은 질문

How can the tight differential privacy guarantee derived for the shuffle model be extended to other types of queries beyond histogram queries

The tight differential privacy guarantee derived for the shuffle model can be extended to other types of queries beyond histogram queries by adapting the framework and analysis to suit the specific query type. The key lies in understanding the underlying mechanisms of the shuffle model and how they interact with different types of queries. For instance, for point queries or range queries, the privacy guarantees would need to consider the specific characteristics of these queries and how the shuffle model can be applied to ensure differential privacy.
One approach to extending the tight differential privacy guarantee to other query types is to analyze the sensitivity of the queries and how they interact with the noise introduced by the shuffle model. By understanding the impact of the shuffle mechanism on different types of queries, it is possible to derive the necessary conditions for achieving tight differential privacy guarantees across a broader range of query types.
Additionally, considering the specific requirements and constraints of different query types, such as aggregation queries or join queries, can help in tailoring the privacy mechanisms to provide the desired level of privacy while maintaining utility. By adapting the concepts and techniques used in the analysis of histogram queries to other query types, it is possible to extend the tight differential privacy guarantees of the shuffle model to a wider range of scenarios.

What are the potential limitations or drawbacks of the shuffle model compared to the central model, beyond the privacy-utility trade-off

While the shuffle model offers a promising approach to balancing privacy and utility in differential privacy mechanisms, there are potential limitations and drawbacks compared to the central model that go beyond the privacy-utility trade-off. Some of these limitations include:

Complexity and Overhead: The shuffle model introduces additional complexity and computational overhead compared to the central model. The shuffling process and coordination among data owners and the server can be resource-intensive and may impact the scalability of the system.

Communication Overhead: The shuffle model requires communication between data owners and the server for the shuffling process, leading to increased communication overhead. This can be a bottleneck in systems with a large number of participants or when dealing with real-time data.

Security Risks: The shuffle model relies on the assumption that the shuffling process is secure and cannot be compromised. Any vulnerabilities in the shuffling mechanism or the communication channels can pose security risks and potentially lead to privacy breaches.

Limited Applicability: The shuffle model may not be suitable for all types of data or queries. Certain types of queries or data structures may not lend themselves well to the shuffling process, limiting the applicability of the shuffle model in diverse scenarios.

Privacy Leakage: Despite providing differential privacy guarantees, the shuffle model may still be susceptible to privacy leakage if not implemented and configured correctly. The effectiveness of the privacy guarantees relies on the proper implementation and parameter settings.

How can the insights from this work on the privacy-utility trade-off be applied to improve the design of practical differential privacy mechanisms for real-world applications

The insights from this work on the privacy-utility trade-off in differential privacy mechanisms can be applied to improve the design of practical mechanisms for real-world applications in the following ways:

Optimizing Noise Addition: By understanding the trade-off between privacy and utility, researchers and practitioners can optimize the amount and type of noise added to data to achieve the desired level of privacy while minimizing the impact on utility. This optimization can lead to more efficient and effective differential privacy mechanisms.

Tailoring Mechanisms to Specific Applications: The analysis of privacy-utility trade-offs can help in tailoring differential privacy mechanisms to specific applications and use cases. By considering the specific requirements and constraints of different scenarios, mechanisms can be customized to provide the best balance between privacy and utility.

Enhancing Privacy Guarantees: The insights gained from studying the privacy-utility trade-off can be used to enhance the privacy guarantees of differential privacy mechanisms. By refining the analysis and understanding the impact of different parameters on privacy and utility, mechanisms can be strengthened to provide stronger privacy protection.

Real-time Monitoring and Adjustment: Understanding the dynamics of privacy-utility trade-offs can enable real-time monitoring and adjustment of differential privacy mechanisms. By continuously evaluating the trade-offs and adjusting parameters based on changing data and query patterns, mechanisms can adapt to ensure optimal performance.

Overall, the insights from this work can inform the design and implementation of differential privacy mechanisms in real-world applications, leading to more effective and privacy-preserving data analysis and sharing practices.