{'fraction_non_alphanumeric': 0.0208183776022972, 'fraction_numerical': 0.0, 'mean_word_length': 6.260416666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
PSCluster: Differentially Private Spatial Cluster Detection for Mobile Crowdsourcing Applications

Spatial cluster detection has been employed to identify significant connected spatial clusters in a variety of crowdsourcing applications in bioinformatics, social networks, and network security. However, despite of providing tremendous social benefits, the release of crowdsourced data will pose a considerable threat to mobile users’ privacy. Differential privacy has been adopted for privacy preservation of crowdsourced data, yet the effective mining of protected data becomes challenging. In this paper, we investigate the problem of spatial cluster detection on privacy-preserving crowdsourced data. Specifically, we propose PSCluster, a differentially private spatial cluster detection mechanism to provide services with data protected under a differential privacy model. PSCluster’s key components include a hypothesis testing framework for modeling differentially private spatial data, and a hybrid algorithm that jointly conducts expectation maximization (EM)-based parameter estimation and free-form spatial cluster detection. We evaluate PSCluster over synthetic data and real data from mobile crowdsourcing applications, and compare PSCluster with two state-of-the-art baseline methods. The experimental results show that PSCluster improves the utility without sacrificing privacy.