Algorithms to anonymize structured medical and healthcare data:A systematic review

Introduction: With many anonymization algorithms developed for structured medical health data (SMHD) in the last decade, our systematic review provides a comprehensive bird’s eye view of algorithms for SMHD anonymization. Methods: This systematic review was conducted according to the recommendations in the Cochrane Handbook for Reviews of Interventions and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Eligible articles from the PubMed, ACM digital library, Medline, IEEE, Embase, Web of Science Collection, Scopus, ProQuest Dissertation, and Theses Global databases were identified through systematic searches. The following parameters were extracted from the eligible studies: author, year of publication, sample size, and relevant algorithms and/or software applied to anonymize SMHD, along with the summary of outcomes. Results: Among 1,804 initial hits, the present study considered 63 records including research articles, reviews, and books. Seventy five evaluated the anonymization of demographic data, 18 assessed diagnosis codes, and 3 assessed genomic data. One of the most common approaches was k-anonymity, which was utilized mainly for demographic data, often in combination with another algorithm; e.g., l-diversity. No approaches have yet been developed for protection against membership disclosure attacks on diagnosis codes. Conclusion: This study reviewed and categorized different anonymization approaches for MHD according to the anonymized data types (demographics, diagnosis codes, and genomic data). Further research is needed to develop more efficient algorithms for the anonymization of diagnosis codes and genomic data. The risk of reidentification can be minimized with adequate application of the addressed anonymization approaches. Systematic Review Registration: [http://www.crd.york.ac.uk/prospero], identifier [CRD42021228200].</p

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

Privacy preservation in social media environments using big data

With the pervasive use of mobile devices, social media, home assistants, and smart devices, the idea of individual privacy is fading. More than ever, the public is giving up personal information in order to take advantage of what is now considered every day conveniences and ignoring the consequences. Even seemingly harmless information is making headlines for its unauthorized use (18). Among this data is user trajectory data which can be described as a user\u27s location information over a time period (6). This data is generated whenever users access their devices to record their location, query the location of a point of interest, query directions to get to a location, request services to come to their location, and many other applications. This data could be used by a malicious adversary to track a user\u27s movements, location, daily patterns, and learn details personal to the user. While the best course of action would be to hide this information entirely, this data can be used for many beneficial purposes as well. Emergency vehicles could be more efficiently routed based on trajectory patterns, businesses could make intelligent marketing or building decisions, and users themselves could benefit by taking advantage of more conveniences. There are several challenges to publishing this data while also preserving user privacy. For example, while location data has good utility, users expect their data to be private. For real world applications, users generate many terabytes of data every day. To process this volume of data for later use and anonymize it in order to hide individual user identities, this thesis presents an efficient algorithm to change the processing time for anonymization from days, as seen in (20), to a matter of minutes or hours. We cannot focus just on location data, however. Social media has a great many uses, one of which being the sharing of images. Privacy cannot stop with location, but must reach to other data as well. This thesis addresses the issue of image privacy in this work, as often images can be even more sensitive than location --Abstract, page iv

Large-scale Wireless Local-area Network Measurement and Privacy Analysis

The edge of the Internet is increasingly becoming wireless. Understanding the wireless edge is therefore important for understanding the performance and security aspects of the Internet experience. This need is especially necessary for enterprise-wide wireless local-area networks (WLANs) as organizations increasingly depend on WLANs for mission- critical tasks. To study a live production WLAN, especially a large-scale network, is a difficult undertaking. Two fundamental difficulties involved are (1) building a scalable network measurement infrastructure to collect traces from a large-scale production WLAN, and (2) preserving user privacy while sharing these collected traces to the network research community. In this dissertation, we present our experience in designing and implementing one of the largest distributed WLAN measurement systems in the United States, the Dartmouth Internet Security Testbed (DIST), with a particular focus on our solutions to the challenges of efficiency, scalability, and security. We also present an extensive evaluation of the DIST system. To understand the severity of some potential trace-sharing risks for an enterprise-wide large-scale wireless network, we conduct privacy analysis on one kind of wireless network traces, a user-association log, collected from a large-scale WLAN. We introduce a machine-learning based approach that can extract and quantify sensitive information from a user-association log, even though it is sanitized. Finally, we present a case study that evaluates the tradeoff between utility and privacy on WLAN trace sanitization