An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream

Abstract

Recently, various services based on user's location are emerging since the development of wireless Internet and sensor technology. VANET (vehicular ad hoc network), in which a large number of vehicles communicate using wireless communication, is also being highlighted as one of the services. VANET collects and analyzes the traffic data periodically to provide the traffic information service. The problem is that traffic data contains user’s sensitive location information that can lead to privacy violations. Differential privacy techniques are being used as a de facto standard to prevent such privacy violation caused by data analysis. However, applying differential privacy to traffic data stream which has infinite size over time makes data useless because too much noise is inserted to protect privacy. In order to overcome this limitation, existing researches set a certain range of windows and apply differential privacy to windowed data. However, previous researches have set a fixed window size do not consider a traffic data’s property such as road structure and time-based traffic variation. It may lead to insufficient privacy protection and unnecessary data utility degradation. In this paper, we propose an adaptive window size selection method that consider the correlation between road networks and time-based traffic variation to solve a fixed window size problem. And we suggest an adjustable privacy budget allocation technique for corresponding to the adaptive window size selection. We show that the proposed method improves the data utility, while satisfying the equal level of differential privacy as compared with the existing method through experiments that is designed based on real-world road network.

An Analysis of Differential Privacy Research in Location and Trajectory Data

“…The sliding window approach is also used in [81], but with variable size windows. The solution considers traffic data property, such as road structure and time-based traffic variation to adaptively select the window size.…”

“…Trajectory data analysis interval-based methods [74], [75], [74], and [76] Achieves aggregates indistinguishability while factoring in the predicted aggregates at each timestamp (C3) The need for a centralized curator may create privacy attack opportunities sliding window methods [78], [79], [80], [81], and [ reveal or conceal pieces of their location data. For LBS applications, [91] shows that for different types of LBS, the privacy leak can be on the location, or on the query or both.…”

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

“…Among the machine learning techniques suggested in [11] for identifying fake websites are (Lagrangian Support Vector Machine) LSVM, (Logistic regression) LR, Random Forest (RF), Naive Bayes (NB), and statistical techniques for finding Concept Drifts in websites. Only a few studies have produced practical results, according to the author in [12], intending to foster research on intelligent security techniques based on a cyclic process that begins with the discovery of new threats and ends with the analysis and development of prevention measures. The authors of [13] propose a novel Gradient Boosting Decision Tree (GBDT) training technique with narrower sensitivity limitations and much better noise allocations.…”

Differential privacy EV charging data release based on variable window

“…Literature (Cao et al, 2017) quantified the differential privacy risk under time correlation for the influence of time correlation in a continuous data release (Acs & Castelluccia, 2014;Ma et al, 2019;Wang, Sinnott & Nepal, 2017;Jo, Jung & Park, 2018). studied the release of user trajectory data, and proposed a space-based differential privacy protection mechanism through the spatial correlation of trajectory data, but it is not applicable.…”