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1. Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets

   https://doi.org/10.1186/s12942-020-00201-9  

   Wang, Jue ; Kwan, Mei-Po ( December 2020 , International Journal of Health Geographics)

   null (Ed.)

   Abstract Background Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals’ geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method. Methods This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios. Results The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual’s home and all the other daily activity locations. Conclusions This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing. 

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2. Analysis of year-over-year changes in Risk Factors Disclosure in 10-K filings

   https://doi.org/10.1145/3220547.3220555  

   Rawte, Vipula ; Gupta, Aparna ; Zaki, Mohammed J. ( June 2018 , Proceeding DSMM'18 Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets)
3. Generating Partially Synthetic Geocoded Public Use Data with Decreased Disclosure Risk by Using Differential Smoothing

   https://doi.org/10.1111/rssa.12360  

   Quick, Harrison ; Holan, Scott H. ; Wikle, Christopher K. ( March 2018 , Journal of the Royal Statistical Society Series A: Statistics in Society)

   When collecting geocoded confidential data with the intent to disseminate, agencies often resort to altering the geographies before making data publicly available. An alternative to releasing aggregated and/or perturbed data is to release synthetic data, where sensitive values are replaced with draws from models designed to capture distributional features in the data collected. The issues associated with spatially outlying observations in the data, however, have received relatively little attention. Our goal here is to shed light on this problem, to propose a solution—referred to as ‘differential smoothing’—and to illustrate our approach by using sale prices of homes in San Francisco.

    

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4. On the Linear Capacity of Conditional Disclosure of Secrets

   https://doi.org/10.1109/ISIT45174.2021.9517867  

   Li, Zhou ; Sun, Hua ( July 2021 , 2021 IEEE International Symposium on Information Theory (ISIT))
5. A study of self-disclosure during the Coronavirus pandemic

   https://doi.org/10.5210/fm.v26i7.11555  

   Blose, Taylor ; Umar, Prasanna ; Squicciarini, Anna ; Rajtmajer, Sarah ( July 2021 , First Monday)

   We study observed incidence of self-disclosure in a large set of tweets representing user-led English-language conversation about the Coronavirus pandemic. Using an unsupervised approach to detect voluntary disclosure of personal information, we provide early evidence that situational factors surrounding the Coronavirus pandemic may impact individuals’ privacy calculus. Text analyses reveal topical shift toward supportiveness and support-seeking in self-disclosing conversation on Twitter. We run a comparable analysis of tweets from Hurricane Harvey to provide context for observed effects and suggest opportunities for further study. 

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