Towards Purpose-aware Privacy-Preserving Techniques for Predictive Applications

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In the field of machine learning (ML), the goal is to leverage algorithmic models to generate predictions, transforming raw input data into valuable insights. However, the ML pipeline, consisting of input data, models, and output data, is susceptible to various vulnerabilities and attacks. These attacks include re-identification, attribute inference, membership inference, and model inversion attacks, all posing threats to individual privacy. This thesis specifically targets attribute inference attacks, wherein adversaries seek to infer sensitive information about target individuals.

The literature on privacy-preserving techniques explores various perturbative approaches, including obfuscation, randomization, and differential privacy, to mitigate privacy attacks. While these methods have shown effectiveness, conventional perturbation based techniques often offer generic protection, lacking the nuance needed to preserve specific utility and accuracy. These conventional techniques are typically purpose unaware, meaning they modify data to protect privacy while maintaining general data usefulness. Recently, there has been a growing interest in purpose-aware techniques.
The thesis introduces purpose-aware privacy preservation in the form of a conceptual framework. This approach involves tailoring data modifications to serve specific purposes and implementing changes orthogonal to relevant features. We aim to protect user privacy without compromising utility. We focus on two key applications within the ML spectrum: recommender systems and machine learning classifiers. The objective is to protect these applications against potential privacy attacks, addressing vulnerabilities in both input data and output data (i.e., predictions).

We structure the thesis into two parts, each addressing distinct challenges in the ML pipeline.
Part 1 tackles attacks on input data, exploring methods to protect sensitive information while maintaining the accuracy of ML models, specifically in recommender systems. Firstly, we explore an attack scenario in which an adversary can acquire the user-item matrix and aims to infer privacy-sensitive information. We assume that the adversary has a gender classifier that is pre-trained on unprotected data. The objective of the adversary is to infer the gender of target individuals. We propose personalized blurring (PerBlur), a personalization-based approach to gender obfuscation that aims to protect user privacy while maintaining the recommendation quality. We demonstrate that recommender system algorithms trained on obfuscated data perform comparably to those trained on the original user-item matrix.
Furthermore, our approach not only prevents classifiers from predicting users' gender based on the obfuscated data but also achieves diversity through the recommendation of (non-stereotypical) diverse items. Secondly, we investigate an attack scenario in which an adversary has access to a user-item matrix and aims to exploit the user preference values that it contains. The objective of the adversary is to infer the preferences of individual users. We propose Shuffle-NNN, a data masking-based approach that aims to hide the preferences of users for individual items while maintaining the relative performance of recommendation algorithms. We demonstrate that Shuffle-NNN provides evidence of what information should be retained and what can be removed from the user-item matrix. Shuffle-NNN has great potential for data release, such as in data science challenges.

Part 2 investigates attacks on output data, focusing on model inversion attacks aimed at predictions from machine learning classifiers and examining potential privacy risks associated with recommender system outputs. Firstly, we explore a scenario where an adversary attempts to infer individuals' sensitive information by querying a machine learning model and receiving output predictions. We investigate various attack models and identify a potential risk of sensitive information leakage when the target model is trained on original data. To mitigate this risk, we propose to replace the original training data with protected data using synthetic training data + privacy-preserving techniques. We show that the target model trained on protected data achieves performance comparable to the target model trained on original data. We demonstrate that by using privacy-preserving techniques on synthetic training data, we observe a small reduction in the success of certain model inversion attacks measured over a group of target individuals. Secondly, we explore an attack scenario in which the adversary seeks to infer users' sensitive information by intercepting recommendations provided by a recommender system to a set of users. Our goal is to gain insight into possible unintended consequences of using user attributes as side information in context-aware recommender systems. We study the extent to which personal attributes of a user can be inferred from a list of recommendations to that user. We find that both standard recommenders and context-aware recommenders leak personal user information into the recommendation lists.
We demonstrate that using user attributes in context-aware recommendations yields a small gain in accuracy. However, the benefit of this gain is distributed unevenly among users and it sacrifices coverage and diversity. This leads us to question the actual value of side information and the need to ensure that there are no hidden `side effects'.

The final chapter of the thesis summarizes our findings. It provides recommendations for future research directions which we think are promising for further exploring and promoting the use of purpose-aware privacy-preserving data for ML predictions.