Balancing Privacy and Utility Tradeoffs for Distinct User Groups Through Collaborative Data Sanitization

核心概念

The core message of this paper is to introduce a novel problem formulation and a data sharing mechanism that enables highly accurate predictions of utility features while simultaneously safeguarding the privacy of distinct user groups, without relying on auxiliary datasets or manual data annotations.

要約

The paper presents a novel problem formulation that addresses the privacy-utility tradeoff in a scenario involving two distinct user groups, each with unique sets of private and utility attributes. Unlike previous studies that focused on single-group settings, this work introduces a collaborative data-sharing mechanism facilitated by a trusted third-party service provider.

The key highlights and insights are:

1. The proposed data-sharing mechanism does not require the third-party to have access to any auxiliary datasets or manually annotate the data. Instead, it leverages the data from the two user groups to train separate privacy mechanisms for each group.

2. The privacy mechanism is trained using adversarial optimization techniques, similar to existing approaches like ALFR and UAE-PUPET, but adapted for the two-group setting.

3. Experimental results on synthetic and real-world datasets (US Census) demonstrate the effectiveness of the proposed approach in achieving high accuracy for utility features while significantly reducing the accuracy of private feature predictions, even when analysts have access to auxiliary datasets.

4. The data-sharing mechanism is compatible with various existing adversarially trained privacy techniques, and the authors show that the UAE-PUPET technique outperforms ALFR within the proposed framework.

5. The paper also provides insights into the privacy-utility tradeoffs using information-theoretic measures like mutual information and established metrics like privacy leakage, utility performance, and privacy-utility tradeoff.

6. Visualization of the sanitized data in two-dimensional space further highlights the effectiveness of the proposed approach in preserving utility while obfuscating private features.

統計

The authors report the following key statistics:
"For G1 in the US Census dataset without any data sanitization (no privacy), the accuracy is 0.88 for private features and 0.92 for utility features. However, after applying the data sharing mechanism using ALFR, the accuracy for private features drops to 0.58, and it drops to 0.55 when using the UAE-PUPET technique. In contrast, the accuracy for utility features remains high at 0.87 with ALFR and 0.90 with UAE-PUPET."
"For G2, the accuracy for private features decreases from 0.98 to 0.60 with ALFR and to 0.59 with UAE-PUPET, while the accuracy for utility features experiences a slight decrease from 0.93 to 0.88 with ALFR and 0.89 with UAE-PUPET."

引用

"Our methodology ensures that private attributes cannot be accurately inferred while enabling highly accurate predictions of utility features."
"Importantly, even if analysts or adversaries possess auxiliary datasets containing raw data, they are unable to accurately deduce private features."

抽出されたキーインサイト

Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization

by Bishwas Mand... 場所 arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05043.pdf

深掘り質問

How can the proposed data-sharing mechanism be extended to handle scenarios with more than two distinct user groups

The proposed data-sharing mechanism can be extended to handle scenarios with more than two distinct user groups by implementing a multi-group data-sharing approach. In this extended scenario, each user group would have its unique set of private and utility features. The data-sharing mechanism would need to be modified to accommodate multiple groups by incorporating additional classifiers for each group's private and utility features. The iterative process of training the privacy mechanisms and generating sanitized data would involve alternating between the different groups, ensuring that each group's data is used to train the privacy mechanism for the other groups. By expanding the data-sharing mechanism to multiple user groups, the privacy and utility tradeoffs can be optimized across a more diverse and complex dataset.

What are the potential challenges and considerations in developing a unified adversarial architecture for privacy mechanism training in multi-group settings, and how can they be addressed

Developing a unified adversarial architecture for privacy mechanism training in multi-group settings presents several challenges and considerations. One key challenge is the potential instability of the adversarial training process when multiple groups with varying private and utility features are involved. To address this, it is essential to carefully design the architecture to handle the complexities of multi-group scenarios, such as incorporating separate discriminators for each group's private and utility features. Additionally, ensuring convergence and avoiding mode collapse in the training process requires fine-tuning the hyperparameters and optimizing the loss functions for each group. Balancing the tradeoff between privacy and utility across multiple groups also requires a nuanced approach, as the objectives and constraints may differ for each group. By carefully addressing these challenges and considerations, a unified adversarial architecture can effectively train privacy mechanisms in multi-group settings.

Given the philosophical disagreements around the distinctiveness of private and utility features across user groups, how can the problem formulation be adapted to accommodate different perspectives on this aspect

To accommodate different perspectives on the distinctiveness of private and utility features across user groups, the problem formulation can be adapted by introducing customizable parameters that allow for varying levels of differentiation between private and utility attributes. This customization can be achieved by incorporating adjustable weights or constraints in the privacy mechanism training process, enabling users to specify the importance of privacy versus utility based on their specific requirements. By providing flexibility in defining the tradeoff between privacy and utility, the problem formulation can cater to diverse philosophical viewpoints and privacy preferences within different user groups. Additionally, incorporating a mechanism for users to input their preferences or constraints regarding the handling of private and utility features can enhance the adaptability and inclusivity of the privacy-utility tradeoff optimization process.