On the Anonymization of Differentially Private Location Obfuscation

Obfuscation techniques in location-based services (LBSs) have been shown useful to hide the concrete locations of service users, whereas they do not necessarily provide the anonymity. We quantify the anonymity of the location data obfuscated by the planar Laplacian mechanism and that by the optimal geo-indistinguishable mechanism of Bordenabe et al. We empirically show that the latter provides stronger anonymity than the former in the sense that more users in the database satisfy k-anonymity. To formalize and analyze such approximate anonymity we introduce the notion of asymptotic anonymity. Then we show that the location data obfuscated by the optimal geo-indistinguishable mechanism can be anonymized by removing a smaller number of users from the database. Furthermore, we demonstrate that the optimal geo-indistinguishable mechanism has better utility both for users and for data analysts.Comment: ISITA'18 conference pape

Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions

LoCoH: nonparameteric kernel methods for constructing home ranges and utilization distributions.

Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: "fixed sphere-of-influence," or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an "adaptive sphere-of-influence," or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original "fixed-number-of-points," or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu)

Ranking the Schools: How Quality Information Affects School Choice in the Netherlands

This paper analyzes whether information on high school quality published by a national newspaper affects school choice in the Netherlands. For this purpose, we use both school level and individual student level data. First, we study the causal effect of quality scores on the influx of new high school students using a longitudinal school dataset. We find that negative (positive) school quality scores decrease (increase) the number of students choosing a school after the year of publication. The positive effects are particularly large for the academic school track. An academic school track receiving the most positive score sees its inflow of students rise by 15 to 20 students. Second, we study individual school choice behaviour to address the relative importance of the quality scores, as well as potential differences in the quality response between socio-economic groups. Although the probability of attending a school is affected by its quality score, it is mainly driven by the travelling distance. Students are only willing to travel about 200 meters more in order to attend a well-performing rather than an average school. In contrast to equity concerns that are often raised, we cannot find differences in information responses between socio-economic groups.school quality, school choice, information, media

The location of innovative activity in Europe

In this paper we use new data to describe how firms from 15 European countries organise their innovative activities. The data matches firm level accounting data with information on the patents that those firms and their subsidiaries have applied for at the European Patents Office. We describe the data in detail

Location Prediction: Communities Speak Louder than Friends

Humans are social animals, they interact with different communities of friends to conduct different activities. The literature shows that human mobility is constrained by their social relations. In this paper, we investigate the social impact of a person's communities on his mobility, instead of all friends from his online social networks. This study can be particularly useful, as certain social behaviors are influenced by specific communities but not all friends. To achieve our goal, we first develop a measure to characterize a person's social diversity, which we term `community entropy'. Through analysis of two real-life datasets, we demonstrate that a person's mobility is influenced only by a small fraction of his communities and the influence depends on the social contexts of the communities. We then exploit machine learning techniques to predict users' future movement based on their communities' information. Extensive experiments demonstrate the prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201

Ranking the schools: How quality information affects school choice in the Netherlands

Both school level and individual student level data indicate that information on highschool quality published by a national newspaper affects school choice in the Netherlands. The positive effects are particularly large for the academic school track. First, we study the causal effect of quality scores on the influx of new highschool students using a longitudinal school dataset. We find that negative (positive) school quality scores decrease (increase) the number of students choosing a school after the year of publication. An academic school track receiving the most positive score sees its inflow of students rise by 15 to 20 students. Second, we study individual school choice behavior to address the relative importance of the quality scores, as well as potential differences in the quality response between socio-economic groups. Although the probability of attending a school is affected by its quality score, it is mainly driven by the traveling distance. Students are willing to travel only about 200 meters more to attend a well-performing rather than an average school. In contrast to equity concerns that are often raised, we cannot find differences in information responses between socio-economic groups.

Choice and the composition of general practice patient registers

Choice of general practice (GP) in the National Health Service (NHS), the UKs universal healthcare service, is a core element in the current trajectory of NHS policy. This paper uses an accessibility-based approach to investigate the pattern of patient choice that exists for GPs in the London Borough of Southwark. Using a spatial model of GP accessibility it is shown that particular population groups make non-accessibility based decisions when choosing a GP. These patterns are assessed by considering differences in the composition of GP patient registers between the current patient register, and a modelled patient register configured for optimal access to GPs. The patient population is classified in two ways for the purpose of this analysis: by geodemographic group, and by ethnicity. The paper considers choice in healthcare for intra-urban areas, focusing on the role of accessibility and equity