With the increasing quantity of private cars, curb parking has evolved into an important approach to mitigate parking pressure in urban cities. While some efforts have been made for the demand analysis of point-of-interest (POI) and pattern analysis of human mobility, which may indirectly reflect the parking situation in urban area, there is a lack of comprehensive models for the parking demand, so as to make a prediction for the road sections without parking lots. In this paper, by focusing on curb parking and designing a systemic framework, namedCurb Parking Demand Estimation(CPDE), we model the public parking demand in urban area, w.r.t. parking durations and regional characteristics. Specifically, we use taxi destinations and the distribution of POIs to quantitatively analyze the regional characteristics, designing corresponding features, and propose aK-means-basedLeast Square (KLS) method to relate parking characteristics, namely, the temporal parking durations and the corresponding demands, with these features. In this way, we effectively avoid the geographical sparsity of road parking sections and can finely estimate parking durations and demands for newly developed districts without parking data. Moreover, we give a strategy, namedParking Types Estimation(PTE), which projects estimated parking durations and demands onto Gaussian Mixture Model (GMM) to accurately measure the distribution of demands over different parking durations for a road section. At last, we conduct experiments on a real-world curb parking dataset in Hefei, a provincial city in China. This dataset contains parking orders of 2016 over the urban area of Hefei. The experimental results validate the effectiveness of our methods, and show that our framework outperforms the state-of-the-art baseline schemes.

Local differential privacy (LDP), as a strong and practical notion, has been applied to deal with privacy issues in data collection. However, existing LDP-based strategies mainly focus on utility optimization at a single privacy level while ignoring various privacy preferences of data providers and multilevel privacy demands for statistics. In this poster, we for the first time propose a framework to optimize the utility of histogram estimation with these two privacy requirements. To clarify the goal of privacy protection, we personalize the traditional definition of LDP. We design two independent approaches to minimize the utility loss: Advanced Combination, which composes multilevel results for utility optimization, and Data Recycle with Personalized Privacy, which enlarges sample size for an estimation. We demonstrate their effectiveness on privacy and utility. Moreover, we embed these approaches within a Recycle and Combination Framework and prove that the framework stably achieves the optimal utility by quantifying its error bounds. On real-world datasets, our approaches are experimentally validated and remarkably outperform baseline methods.

Recently, local differential privacy (LDP), as a strong and practical notion, has been applied to deal with privacy issues in data collection. However, existing LDP-based strategies mainly focus on utility optimization at a single privacy level while ignoring various privacy preferences of data providers and multilevel privacy demands for statistics. In this paper, we for the first time propose a framework to optimize the utility of histogram estimation with these two privacy requirements. To clarify the goal of privacy protection, we personalize the traditional definition of LDP. We design two independent approaches to minimize the utility loss: Advanced Combination, which composes multilevel results for utility optimization, and Data Recycle with Personalized Privacy, which enlarges the sample size for an estimation. We demonstrate their effectiveness on privacy and utility, respectively. Moreover, we embed these approaches within a Recycle and Combination Framework and prove that the framework stably achieves the optimal utility by quantifying its error bounds. On real-world datasets, our approaches are experimentally validated and remarkably outperform baseline methods.

For the purpose of improving the quality of services, softwares or online services are collecting various of user data, such as personal information and locations. Such data facilitates mining statistical knowledge of users, but threatens users' privacy as it may reveal sensitive information (e.g., identities and activities) about individuals. This work considers distribution estimation over user-contributed data meanwhile providing rigid protection of their data with local ε-differential privacy (ε-LDP), which sanitizes each user's data on the client's side (e.g, on the user's mobile device). Our privacy protection covers both qualitative data (e.g., categorical data) and discrete quantitative data (e.g., location data). Specifically, for categorical data, we derive an optimal ε-LDP mechanism (termed as k-subset mechanism) from mutual information perspective, and further show its optimality over existing approaches within the context of discrete distribution estimation; for discrete quantitative data that have arbitrary distance metric, we provide an efficient extension of k-subset mechanism by proposing a variant of the popular Exponential Mechanism (EM) to tackle the asymmetry issue on the data domain. Experiments on real-world datasets and simulated scenarios show that our mechanism is highly efficient and reduces nearly a fraction of exp(- ε/2) error for distribution estimation when compared to existing approaches.

Since urban rail unmanned train is considered as one of the core technologies of Intelligent Transportation System (ITS) , how to shorten the train headway as the driven interval between unmanned trains properly is still a challenge in Communications Based Train Control (CBTC) system. In this paper, we focus on analyzing the main factors which affect the train headway in inter-station barrier tracking mode, interstation stop tracking mode and station tracking mode and present an optimization scheme for Automatic Train Control (ATC) system with mobile block technology. Simulation results show that the proposed optimization scheme can reduce train headway to improve operational efficiency and reduce costs of urban rail transit system.

Set-valued data is useful for representing a rich family of information in numerous areas, such as market basket data of online shopping, apps on mobile phones and web browsing history. By analyzing set-valued data that are collected from users, service providers could learn the demographics of the users, the patterns of their usages, and finally, improve the quality of services for them. However, privacy has been an increasing concern in collecting and analyzing users' set-valued data, since these data may reveal sensitive information (e.g., identities, preferences and diseases) about individuals. In this work, we propose a privacy preserving aggregation mechanism for set-valued data: PrivSet. It provides rigorous data privacy protection locally (e.g., on mobile phones or wearable devices) and efficiently (its computational overhead is linear to the item domain size) for each user, and meanwhile allowing effective statistical analyses (e.g., distribution estimation of items, distribution estimation of set cardinality) on set-valued data for service providers. More specifically, in PrivSet, within the constraints of local e-differential privacy, each user independently responses with a subset of the set-valued data domain with calibrated probabilities, hence the true positive/false positive rate of each item is balanced and the performance of distribution estimation is optimized. Besides presenting theoretical error bounds of PrivSet and proving its optimality over existing approaches, we experimentally validate the mechanism, the experimental results illustrate that the estimation error in PrivSet has been reduced by half when compared to state-of-the-art approaches.

The categorical data that have natural ordering between categories are termed ordinal data, which are pervasive in numerous areas, including discrete sensor readings, metering data or preference options. Though aggregating such ordinal data from the population is facilitating plenty of crowdsourcing applications, contributing such data is privacy risky and may reveal sensitive information (e.g. locations, identities) about individuals. This work studies ordinal data aggregation for distribution estimation meanwhile locally preserving individuals' data privacy (such as on their mobile devices). Under ε-geo-indistinguishable constraints, which capture intrinsic dissimilarity between ordinal categories in the framework of differential privacy, we provide an efficient and effective locally private mechanism: Subset Exponential Mechanism (SEM) for ordinal data distribution estimation. The mechanism randomly responds with a fixed-size subset of the categories with calibrated probability assignment. Specially for uniform ordinal data, we propose a circling technique to symmetrically randomizing categories and estimating frequencies of categories, hence the computational/space costs and estimation performance of SEM are further optimized. Besides contributing theoretical error bounds of SEM, we also evaluate the mechanism on extensive scenarios, the evaluation results show that SEM reduces distribution estimation error on average by exp(ϵ/2) factor over existing private mechanisms.