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1. Analysis of Privacy Protections in Fitness Tracking Social Networks -or You can run, but can you hide?

   Hassan, Wajih Ul ; Hussain, Saad ; Bates, Adam ( August 2018 , 27th USENIX Security Symposium)

   Mobile fitness tracking apps allow users to track their workouts and share them with friends through online social networks. Although the sharing of personal data is an inherent risk in all social networks, the dangers presented by sharing personal workouts comprised of geospatial and health data may prove especially grave. While fitness apps offer a variety of privacy features, at present it is unclear if these countermeasures are sufficient to thwart a determined attacker, nor is it clear how many of these services’ users are at risk. In this work, we perform a systematic analysis of privacy behaviors and threats in fitness tracking social networks. Collecting a month-long snapshot of public posts of a popular fitness tracking service (21 million posts, 3 million users), we observe that 16.5% of users make use of Endpoint Privacy Zones (EPZs), which conceal fitness activity near user-designated sensitive locations (e.g., home, office). We go on to develop an attack against EPZs that infers users’ protected locations from the remaining available information in public posts, discovering that 95.1% of moderately active users are at risk of having their protected locations extracted by an attacker. Finally, we consider the efficacy of state-of-the-art privacy mechanisms through adapting geo-indistinguishability techniques as well as developing a novel EPZ fuzzing technique. The affected companies have been notified of the discovered vulnerabilities and at the time of publication have incorporated our proposed countermeasures into their production systems. 

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2. Estimating encounter location distributions from animal tracking data

   https://doi.org/10.1111/2041-210X.13597  

   Noonan, Michael J. ; Martinez‐Garcia, Ricardo ; Davis, Grace H. ; Crofoot, Margaret C. ; Kays, Roland ; Hirsch, Ben T. ; Caillaud, Damien ; Payne, Eric ; Sih, Andrew ; Sinn, David L. ; et al ( May 2021 , Methods in Ecology and Evolution)

   Ecologists have long been interested in linking individual behaviour with higher level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modelling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounterrateswhile the relationship between individual movement and the spatiallocationsof encounter events in the environment has remained conspicuously understudied.

   Here, we bridge this gap by introducing a method for describing the long‐term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open‐source software and demonstrate the broad ecological relevance of this distribution.

   We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation‐based scenarios that occur routinely in biological systems: (a) a population of individuals with home ranges that overlap with neighbours; (b) a pair of individuals with a hard territorial border between their home ranges; and (c) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white‐faced capuchinsCebus capucinus, tracked on Barro Colorado Island, Panama, and sleepy lizardsTiliqua rugosa,tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the potential for competitive or predatory interactions and/or identify any changes in behaviour that directly result from location‐specific encounter probability.

   The CDE enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialized data collection protocols. This method is now openly available via thectmm Rpackage.

    

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3. Sharing personal ECG time-series data privately

   https://doi.org/10.1093/jamia/ocac047  

   Bonomi, Luca ; Wu, Zeyun ; Fan, Liyue ( April 2022 , Journal of the American Medical Informatics Association)

   Emerging technologies (eg, wearable devices) have made it possible to collect data directly from individuals (eg, time-series), providing new insights on the health and well-being of individual patients. Broadening the access to these data would facilitate the integration with existing data sources (eg, clinical and genomic data) and advance medical research. Compared to traditional health data, these data are collected directly from individuals, are highly unique and provide fine-grained information, posing new privacy challenges. In this work, we study the applicability of a novel privacy model to enable individual-level time-series data sharing while maintaining the usability for data analytics.

   We propose a privacy-protecting method for sharing individual-level electrocardiography (ECG) time-series data, which leverages dimensional reduction technique and random sampling to achieve provable privacy protection. We show that our solution provides strong privacy protection against an informed adversarial model while enabling useful aggregate-level analysis.

   We conduct our evaluations on 2 real-world ECG datasets. Our empirical results show that the privacy risk is significantly reduced after sanitization while the data usability is retained for a variety of clinical tasks (eg, predictive modeling and clustering).

   Our study investigates the privacy risk in sharing individual-level ECG time-series data. We demonstrate that individual-level data can be highly unique, requiring new privacy solutions to protect data contributors.

   The results suggest our proposed privacy-protection method provides strong privacy protections while preserving the usefulness of the data.

    

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4. Remote-Region Interior Alaska Community Survey with Two Statistical Measures: Arctic Indigenous Entrepreneurial Readiness (ER); and Remote-Region Digital Technology Needs and Skills (DT).

   https://doi.org/10.18739/A20R9M54S  

   Pasch, Timothy ; Kuhlke, Olaf ; Linton, Renee ( January 2021 , Arctic Data Center)

   Between 2018 and 2021 PIs for National Science Foundation Awards # 1758781 and 1758814 EAGER: Collaborative Research: Developing and Testing an Incubator for Digital Entrepreneurship in Remote Communities, in partnership with the Tanana Chiefs Conference, the traditional tribal consortium of the 42 villages of Interior Alaska, jointly developed and conducted large-scale digital and in-person surveys of multiple Alaskan interior communities. The survey was distributed via a combination of in-person paper surveys, digital surveys, social media links, verbal in-person interviews and telephone-based responses. Analysis of this measure using SAS demonstrated the statistically significant need for enhanced digital infrastructure and reworked digital entrepreneurial and technological education in the Tanana Chiefs Conference region. 1. Two statistical measures were created during this research: Entrepreneurial Readiness (ER) and Digital Technology needs and skills (DT), both of which showed high measures of internal consistency (.89, .81). 2. The measures revealed entrepreneurial readiness challenges and evidence of specific addressable barriers that are currently preventing (serving as hindrances) to regional digital economic activity. The survey data showed statistically significant correlation with the mixed-methodological in-person focus groups and interview research conducted by the PIs and TCC collaborators in Hughes and Huslia, AK, which further corroborated stated barriers to entrepreneurship development in the region. 3. Data generated by the survey and fieldwork is maintained by the Tanana Chiefs Conference under data sovereignty agreements. The survey and focus group data contains aggregated statistical/empirical data as well as qualitative/subjective detail that runs the risk of becoming personally identifiable especially due to (but not limited to) to concerns with exceedingly small Arctic community population sizes. 4. This metadata is being provided in order to serve as a record of the data collection and analysis conducted, and also to share some high-level findings that, while revealing no personal information, may be helpful for policymaking, regional planning and efforts towards educational curricular development and infrastructural investment. The sample demographics consist of 272 women, 79 men, and 4 with gender not indicated as a response. Barriers to Entrepreneurial Readiness were a component of the measure. Lack of education is the #1 barrier, followed closely by lack of access to childcare. Among women who participated in the survey measure, 30% with 2 or more children report lack of childcare to be a significant barrier to entrepreneurial and small business activity. For entrepreneurial readiness and digital economy, the scales perform well from a psychometric standpoint. The summary scores are roughly normally distributed. Cronbach’s alphas are greater than 0.80 for both. They are moderately correlated with each other (r = 0.48, p < .0001). Men and women do not differ significantly on either measure. Education is significantly related to the digital economy measure. The detail provided in the survey related to educational needs enabled optimized development of the Incubator for Digital Entrepreneurship in Remote Communities. Enhanced digital entrepreneurship training with clear cultural linkages to traditions and community needs, along with additional childcare opportunities are two among several specific recommendations provided to the TCC. The project PIs are working closely with the TCC administration and community members related to elements of culturally-aligned curricular development that respects data tribal sovereignty, local data management protocols, data anonymity and adherence to human subjects (IRB) protocols. While the survey data is currently embargoed and unable to be submitted publicly for reasons of anonymity, the project PIs are working with the NSF Arctic Data Center towards determining pathways for sharing personally-protected data with the larger scientific community. These approaches may consist of aggregating and digitally anonymizing sensitive data in ways that cannot be de-aggregated and that meet agency and scientific community needs (while also fully respecting and protecting participants’ rights and personal privacy). At present the data sensitivity protocols are not yet adapted to TCC requirements and the datasets will remain in their care. 

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5. Habitat use as indicator of adaptive capacity to climate change

   https://doi.org/10.1111/ddi.13223  

   Teitelbaum, Claire S. ; Sirén, Alexej P. K. ; Coffel, Ethan ; Foster, Jane R. ; Frair, Jacqueline L. ; Hinton, Joseph W. ; Horton, Radley M. ; Kramer, David W. ; Lesk, Corey ; Raymond, Colin ; et al ( January 2021 , Diversity and Distributions)

   Populations of cold‐adapted species at the trailing edges of geographic ranges are particularly vulnerable to the negative effects of climate change from the combination of exposure to warm temperatures and high sensitivity to heat. Many of these species are predicted to decline under future climate scenarios, but they could persist if they can adapt to warming climates either physiologically or behaviourally. We aim to understand local variation in contemporary habitat use and use this information to identify signs of adaptive capacity. We focus on moose (Alces alces), a charismatic species of conservation and public interest.

   The northeastern United States, along the trailing edge of the moose geographic range in North America.

   We compiled data on occurrences and habitat use of moose from remote cameras and GPS collars across the northeastern United States. We use these data to build habitat suitability models at local and regional spatial scales and then to predict future habitat suitability under climate change. We also use fine‐scale GPS data to model relationships between habitat use and temperature on a daily temporal scale and to predict future habitat use.

   We find that habitat suitability for moose will decline under a range of climate change scenarios. However, moose across the region differ in their use of climatic and habitat space, indicating that they could exhibit adaptive capacity. We also find evidence for behavioural responses to weather, where moose increase their use of forested wetland habitats in warmer places and/or times.

   Our results suggest that there will be significant shifts in moose distribution due to climate change. However, if there is spatial variation in thermal tolerance, trailing‐edge populations could adapt to climate change. We highlight that prioritizing certain habitats for conservation (i.e., thermal refuges) could be crucial for this adaptation.

    

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