# SALPPM

**Repository Path**: ioesc/SALPPM

## Basic Information

- **Project Name**: SALPPM
- **Description**: Semantics-aware Location Privacy Preserving: A Differential Privacy Approach
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 0
- **Created**: 2023-05-11
- **Last Updated**: 2023-05-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Semantics-aware Location Privacy Preserving: A Differential Privacy Approach

This is the repository containing the code and data referred in the paper "Semantics-aware Location Privacy Preserving: A Differential Privacy Approach".

The protection of location privacy, as a highly sensitive characteristic of information, has been extensively analyzed and discussed for a significant period of time. Recently, exploiting semantics of locations offers a new dimension to enhance privacy preservation by enabling more effective control over the information disclosed by users. Different from most prior research efforts, which regard the location semantics as a category, in this paper, location semantics is the statistical information about the Points of Interests (PoIs) in the specific location's vicinity, which can be represented as a multi-dimensional vector. Further, Semantic Indistinguishability (Sem-Ind), a more relaxed privacy guarantee for location privacy than Geo-Indistinguishability (Geo-Ind), is derived under the paradigm of differential privacy. Multiple location obfuscation mechanisms, which integrate linear programming and heuristic search, respectively, are proposed to reduce utility loss while ensuring Sem-Ind. Based on the defined utility and privacy metrics, these obfuscation mechanisms are empirically evaluated on two real-world datasets——Geolife and Gowalla. Experimental results indicate that the existing Geo-Ind-based obfuscation mechanisms satisfiy the Sem-Ind at a excessive cost of utility. Conversely, the linear programming-based approach is capable of discovering optimal obfuscation functions, whereas the heuristic algorithms are more efficient in obtaining acceptable utility result.