# When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy

## TL;DR

This paper introduces a privacy-preserving recommendation system for location privacy preferences using local differential privacy and matrix factorization, improving accuracy and privacy guarantees over existing methods.

## Contribution

It proposes a novel method combining matrix factorization with local differential privacy to accurately recommend location privacy preferences while ensuring formal privacy guarantees.

## Key findings

- Improved accuracy in predicting privacy preferences.
- Effective privacy preservation through local differential privacy.
- Validated method on combined datasets with high user diversity.

## Abstract

In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and leakage of sensitive information, so it is necessary to protect the privacy of location information. For this purpose, people should know their location privacy preferences, that is, whether or not he/she can release location information at each place and time. However, it is not easy for each user to make such decisions and it is troublesome to set the privacy preference at each time. Therefore, we propose a method to recommend location privacy preferences for decision making. Comparing to existing method, our method can improve the accuracy of recommendation by using matrix factorization and preserve privacy strictly by local differential privacy, whereas the existing method does not achieve formal privacy guarantee. In addition, we found the best granularity of a location privacy preference, that is, how to express the information in location privacy protection. To evaluate and verify the utility of our method, we have integrated two existing datasets to create a rich information in term of user number. From the results of the evaluation using this dataset, we confirmed that our method can predict location privacy preferences accurately and that it provides a suitable method to define the location privacy preference.