11,003 research outputs found
Towards Standardized Mobility Reports with User-Level Privacy
The importance of human mobility analyses is growing in both research and
practice, especially as applications for urban planning and mobility rely on
them. Aggregate statistics and visualizations play an essential role as
building blocks of data explorations and summary reports, the latter being
increasingly released to third parties such as municipal administrations or in
the context of citizen participation. However, such explorations already pose a
threat to privacy as they reveal potentially sensitive location information,
and thus should not be shared without further privacy measures.
There is a substantial gap between state-of-the-art research on privacy
methods and their utilization in practice. We thus conceptualize a standardized
mobility report with differential privacy guarantees and implement it as
open-source software to enable a privacy-preserving exploration of key aspects
of mobility data in an easily accessible way. Moreover, we evaluate the
benefits of limiting user contributions using three data sets relevant to
research and practice. Our results show that even a strong limit on user
contribution alters the original geospatial distribution only within a
comparatively small range, while significantly reducing the error introduced by
adding noise to achieve privacy guarantees
Voluntary Turnover and Job Performance: Curvilinearity and the Moderating Influences of Salary Growth, Promotions, and Labor Demand
In this study we investigated the relation between job performance and voluntary employee turnover for 5,143 exempt employees in a single firm in the petroleum industry. As hypothesized, we found support for Jackofsky\u27s (1984) curvilinear hypothesis as turnover was higher for low and high performers than it was for average performers. Three potential moderators of this curvilinearity were examined in an attempt to explain conflicting results in the performance turnover literature and contradictory predictions from turnover models. As predicted, pay growth, promotions, and labor demand each differentially influenced the turnover patterns of low, average, and high performers. Most notably, paying high performers according to their performance predicted substantial decrements in turnover. A utility analysis indicated that the benefits of paying high performers according to their performance more than offset the costs and that such an approach was a superior strategy when compared to a more egalitarian pay growth policy
The Role of Landscape Connectivity in Planning and Implementing Conservation and Restoration Priorities. Issues in Ecology
Landscape connectivity, the extent to which a landscape facilitates the movements of organisms and their genes, faces critical threats from both fragmentation and habitat loss. Many conservation efforts focus on protecting and enhancing connectivity to offset the impacts of habitat loss and fragmentation on biodiversity conservation, and to increase the resilience of reserve networks to potential threats associated with climate change. Loss of connectivity can reduce the size and quality of available habitat, impede and disrupt movement (including dispersal) to new habitats, and affect seasonal migration patterns. These changes can lead, in turn, to detrimental effects for populations and species, including decreased carrying capacity, population declines, loss of genetic variation, and ultimately species extinction. Measuring and mapping connectivity is facilitated by a growing number of quantitative approaches that can integrate large amounts of information about organisms’ life histories, habitat quality, and other features essential to evaluating connectivity for a given population or species. However, identifying effective approaches for maintaining and restoring connectivity poses several challenges, and our understanding of how connectivity should be designed to mitigate the impacts of climate change is, as yet, in its infancy. Scientists and managers must confront and overcome several challenges inherent in evaluating and planning for connectivity, including: •characterizing the biology of focal species; •understanding the strengths and the limitations of the models used to evaluate connectivity; •considering spatial and temporal extent in connectivity planning; •using caution in extrapolating results outside of observed conditions; •considering non-linear relationships that can complicate assumed or expected ecological responses; •accounting and planning for anthropogenic change in the landscape; •using well-defined goals and objectives to drive the selection of methods used for evaluating and planning for connectivity; •and communicating to the general public in clear and meaningful language the importance of connectivity to improve awareness and strengthen policies for ensuring conservation. Several aspects of connectivity science deserve additional attention in order to improve the effectiveness of design and implementation. Research on species persistence, behavioral ecology, and community structure is needed to reduce the uncertainty associated with connectivity models. Evaluating and testing connectivity responses to climate change will be critical to achieving conservation goals in the face of the rapid changes that will confront many communities and ecosystems. All of these potential areas of advancement will fall short of conservation goals if we do not effectively incorporate human activities into connectivity planning. While this Issue identifies substantial uncertainties in mapping connectivity and evaluating resilience to climate change, it is also clear that integrating human and natural landscape conservation planning to enhance habitat connectivity is essential for biodiversity conservation
GLOVE: towards privacy-preserving publishing of record-level-truthful mobile phone trajectories
Datasets of mobile phone trajectories collected by network operators offer an unprecedented opportunity to discover new knowledge from the activity of large populations of millions. However, publishing such trajectories also raises significant privacy concerns, as they contain personal data in the form of individual movement patterns. Privacy risks induce network operators to enforce restrictive confidential agreements in the rare occasions when they grant access to collected trajectories, whereas a less involved circulation of these data would fuel research and enable reproducibility in many disciplines. In this work, we contribute a building block toward the design of privacy-preserving datasets of mobile phone trajectories that are truthful at the record level. We present GLOVE, an algorithm that implements k-anonymity, hence solving the crucial unicity problem that affects this type of data while ensuring that the anonymized trajectories correspond to real-life users. GLOVE builds on original insights about the root causes behind the undesirable unicity of mobile phone trajectories, and leverages generalization and suppression to remove them. Proof-of-concept validations with large-scale real-world datasets demonstrate that the approach adopted by GLOVE allows preserving a substantial level of accuracy in the data, higher than that granted by previous methodologies.This work was supported by the AtracciĂłn de Talento Investigador program of the Comunidad de Madrid under Grant No. 2019-T1/TIC-16037 NetSense
Privacy in trajectory micro-data publishing : a survey
We survey the literature on the privacy of trajectory micro-data, i.e.,
spatiotemporal information about the mobility of individuals, whose collection
is becoming increasingly simple and frequent thanks to emerging information and
communication technologies. The focus of our review is on privacy-preserving
data publishing (PPDP), i.e., the publication of databases of trajectory
micro-data that preserve the privacy of the monitored individuals. We classify
and present the literature of attacks against trajectory micro-data, as well as
solutions proposed to date for protecting databases from such attacks. This
paper serves as an introductory reading on a critical subject in an era of
growing awareness about privacy risks connected to digital services, and
provides insights into open problems and future directions for research.Comment: Accepted for publication at Transactions for Data Privac
Towards Mobility Data Science (Vision Paper)
Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from
the metadata. PDF has not been change
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