Research on Trajectory Data Releasing Method via Differential Privacy Based on Spatial Partition

Abstract

A number of security and privacy challenges of cyber system are arising due to the rapidly evolving scale and complexity of modern system and networks. The cyber system is a fundamental ingredient for Internet of Things (IoT) and smart city which are driven by huge amount of data. These data carry a lot of information for mining and analysis, especially trajectory data. If unprotected trajectory data is released, it may disclose user’s personal privacy, such as home, religion, and behavior mode, which will endanger their personal security. Until now, many methods for protecting trajectory information have been proposed. However, these methods have the following deficiencies: (i) they cannot defend against speculative attacks if the attacker’s background knowledge is maximized; (ii) when studying the problem, they made some strong assumptions that did not match the reality; (iii) the implementation algorithm is complicated and the time complexity is high, which means that data cannot be executed quickly when the amount is large. So, in this paper, we propose a spatial partition based method to publish trajectory data via differential privacy. First, by exponential mechanism, we divide location set at the same time into different partitions fast and accurately. Then we propose another effective method to release trajectory in a differential private manner. We design experiment based on the real-life dataset and compare it with existing method. The results show that the trajectory dataset released by our algorithm has better usability while ensuring privacy.

A Wireless Sensor Network for Monitoring Environmental Quality in the Manufacturing Industry

“…By monitoring the electricity consumption during six production processes of typical sewage discharge factories in the jurisdiction, it was found that the major production periods of large sewage discharge enterprises in November 2018 were concentrated in the following four sections: [4][5][6][7][8][9][10][11][12], [15][16][17][18][19][20][21][22][23], [24][25] and [27][28][29][30]. This performance was consistent with the time section when the concentration of pollution factors was increased in our urban environmental information monitoring analysis.…”

“…And for chemical pollutants, for example, the CO concentration was oftentimes inferred from the reading of a metal-oxide based, total volatile organic compound (TVOC) sensor [12]- [16], but the correlation between TVOC and CO has been shown to be weak. While Raman-based approaches may be used to detect many gases simultaneously, techniques based on absorption spectroscopy are the most promising candidates for reliable CO detection [17]- [20]. It has been found that a high degree of sensitivity and specific, quantitative detection can be achieved by tunable diode laser spectroscopy, albeit at high associated costs in terms of optical and computational infrastructure and maintenance [21]- [24].…”

Generative Adversarial Networks

“…The following three solutions were proposed to improve data quality, convergence rate, training stability, and scalability. First, parameter grouping is a common scheme used in differential privacy [44]. A balance between convergence rate and privacy cost could be achieved by carefully grouping the training parameters and clipping over different groups.…”

Yörünge Verisi Yayınlamada Mahremiyet Duyarlı Yeni Bir Model Önerisi ve Uygulaması

“…STM tarafından yayınlanan yörünge veri kümesi üzerinde yapılan testlerde, ortalama bağıl hata metriği kullanılarak iki farklı senaryoya göre deneyler yapılmış ve yüksek veri faydası elde edilmiştir. Han ve arkadaşlarının yaptığı çalışmada [16], literatürdeki çalışmaların eksikliklerinden yola çıkarak, mekânsal parçalama tabanlı diferansiyel mahremiyet duyarlı yeni bir veri yayınlama metodu geliştirilmiştir. Testlerde Microsoft tarafından yayınlanan T-Drive verisi kullanılmış ve Haussdorf uzaklığı ile de veri faydası ölçülmüştür.…”