Ubiquitous surveillance cameras and personal devices have given rise to the vast generation of image data. While sharing the image data can benefit various applications, including intelligent transportation systems and social science research, those images may capture sensitive individual information, such as license plates, identities, etc. Existing image privacy preservation techniques adopt deterministic obfuscation, e.g., pixelization, which can lead to re-identification with well-trained neural networks. In this study, we propose sharing pixelized images with rigorous privacy guarantees. We extend the standard differential privacy notion to image data, which protects individuals, objects, or their features. Empirical evaluation with real-world datasets demonstrates the utility and efficiency of our method; despite its simplicity, our method is shown to effectively reduce the success rate of re-identification attacks.

Spatial Crowdsourcing (SC) is a transformative platform that engages individuals in collecting and analyzing environmental, social and other spatio-temporal information. SC outsources spatio-temporal tasks to a set of workers, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations. However, current solutions require the workers to disclose their locations to untrusted parties. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We address scenarios with both static and dynamic (i.e., moving) datasets of workers. Experimental results on real-world data show that the proposed technique protects location privacy without incurring significant performance overhead.

Sharing real-time aggregate statistics of private data has given much benefit to the public to perform data mining for understanding important phenomena, such as Influenza outbreaks and traffic congestion. However, releasing time-series data with standard differential privacy mechanism has limited utility due to high correlation between data values. We propose FAST, an adaptive system to release real-time aggregate statistics under differential privacy with improved utility. To minimize overall privacy cost, FAST adaptively samples long time-series according to detected data dynamics. To improve the accuracy of data release per time stamp, filtering is used to predict data values at non-sampling points and to estimate true values from noisy observations at sampling points. Our experiments with three real data sets confirm that FAST improves the accuracy of time-series release and has excellent performance even under very small privacy cost.

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Continuous outlier detection in data streams has important applications in fraud detection, network security, and public health. The arrival and departure of data objects in a streaming manner impose new challenges for outlier detection algorithms, especially in time and space efficiency. In the past decade, several studies have been performed to address the problem of distance-based outlier detection in data streams (DODDS), which adopts an unsupervised definition and does not have any distributional assumptions on data values. Our work is motivated by the lack of comparative evaluation among the state-of-the-art algorithms using the same datasets on the same platform. We systematically evaluate the most recent algorithms for DODDS under various stream settings and outlier rates. Our extensive results show that in most settings, the MCOD algorithm offers the superior performance among all the algorithms, including the most recent algorithm Thresh LEAP.

Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in urban environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks. We introduce a taxonomy of several problem variants, such as budget-per-time-period vs. budget-per-campaign and binary-utility vs. distance-based-utility. We study the hardness of the task assignment problem in the offline setting and propose online heuristics which exploits the spatial and temporal knowledge acquired over time. Our experiments are conducted with spatial crowdsourcing workloads generated by the SCAWG tool and extensive results show the effectiveness and efficiency of our proposed solutions.

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Abstract. Sharing real-time traffic data can be of great value to understanding many important phenomena, such as congestion patterns or popular places. To this end, private user data must be aggregated and shared continuously over time with data privacy guarantee. However, releasing time series data with standard differential privacy mechanism can lead to high perturbation error due to the correlation between time stamps. In addition, data sparsity in the spatial domain imposes another challenge to user privacy as well as utility. To address the challenges, we propose a real-time framework that guarantees differential privacy for individual users and releases accurate data for research purposes. We present two estimation algorithms designed to utilize domain knowledge in order to mitigate the effect of perturbation error. Evaluations with simulated traffic data show our solutions outperform existing methods in both utility and computation efficiency, enabling real-time data sharing with strong privacy guarantee.