Ilangovan, G., Ramezani, M., Kum, H.-C. A Benchmarking System to Evaluate the Effectiveness and Efficiency of Machine Learning Algorithms for Record Linkage. AMIA 2020 INFORMATICS SUMMIT. Will be Presented at Annual Symposium 2020 in Nov.
Li, Q., D'Souza, A., Ramezani, M., Schmit, C. and Kum, H.-C. Increasing Transparent and Accountable Use of Data by Quantifying the Actual Privacy Risk in Interactive Record Linkage. AMIA annual symposium proceedings 2019, 1657.
Kum, H.-C., Ragan, E., Giannouchos, T., Schmit, C., Ilangovan, G., Li, Q. Enhancing Privacy in Record Linkage Studies By Meeting the Minimum Necessary Standard. AcademyHealth Annual Research Meeting, 2019, Washington, DC.
Kum, H.-C., Ragan, E., Ilangovan, G., Ramezani, M., Li, Q., and Schmit, C. Enhancing Privacy through an Interactive On-demand Incremental Information Disclosure Interface: Applying Privacy-by-Design to Record Linkage. 2019 the Symposium on Usable Privacy and Security (SOUPS). 23% (=27/119 acceptance rate)
Kum, H.-C. and Ragan, E. (2019). Exploring the Use of Interactive Interfaces and Feedback Mechanisms to Enhance Privacy in Data Workers through Information Accountability. Workshop on Security Information Workers (WSIW 2019) at the Symposium on Usable Privacy and Security (SOUPS).
Giannouchos, T., Kum, H.-C., Ferdinand, A., Schmit, C., Ilangovan, G., Ragan, E. Patients' and Stakeholders' Perceptions of Risk and Benefits of the Privacy Preserving Interactive Record Linkage (PPIRL) Framework. Poster accepted at the Advanced Ethical Research (AER) Conference, 2018, San Diego, CA.
Schmit,C., Kum, H.-C., Ragan, E., Ferdinand, A., Giannouchos, T. (2018).Trusted Third Party Software Approach to Facilitate Data Disclosures Governed by “Minimum Necessary” Legal Stand0ards in Record Linkage Studies. APHA.2018. San Diego, CA.
Giannouchos T., Kum, H.-C, Ferdinand, A., Schmit, C., Ilangovan, G., Ragan, E. (2018) Patients’ and Stakeholders’ Perceptions of Risk and Benefits of the Privacy Preserving Interactive Record Linkage (PPIRL) Framework. AcademyHealth ARM, 2018, Seattle, WA.
Ragan, E., Kum, H.-C., Ilangovan, G., and Wang, H. (2018). Balancing Privacy and Information Disclosure in Interactive Record Linkage with Visual Masking. Proceedings of the SIGCHI conference on Human factors in computing systems. ACM. CHI2018 Honourable Mention Award (top 5% of all submissions)
Our team presenting at Conference on Human Factors in Computing Systems (CHI) 2018. Click here for video.
Kum, H.-C., Schmit, C., Ferdinand, A., Giannouchos, T. (2017). Controlling privacy risk in database studies for human subject protection (HSP) via a privacy budgeting system. 2017 Advancing Ethical Research Conference.
Kum, H.-C., Schmit, C., Giannouchos, T. Understanding the full continuum of identifiable to de-identifiable data in the world of big data to minimize privacy risk in database studies. 2017 Advancing Ethical Research Conference.
Schmit, C Kum, H.-C. The Consent Paradox: the 2018 Common Rule Revisions Leave HIPAA-Covered Researchers in the Dark. 2017 Advancing Ethical Research Conference.
Kum, H.-C. K-Anonymity Based Privacy Risk Budgeting System for Interactive Record Linkage. Isaac Newton Institute for Mathematical Sciences program on Data Linkage and Anonymisation (Fall 2016) Workshop on Data Linkage: Techniques, Challenges and Applications. Cambridge UK. Invited Seminar.
Kum, H.-C., Krishnamurthy A., Machanavajjhala A., and Ahalt S. Social Genome: Putting Big Data to Work for Population Informatics. IEEE Computer Special Outlook Issue. Jan 2014
Kum H.-C., Krishnamurthy A., Machanavajjhala A., Reiter M., and Ahalt S. Privacy preserving interactive record linkage (PPIRL). J Am Med Inform Assoc, 2014;21:212–220. PMCID: PMC3932473
Kum, H.-C., Ahalt, S., and Pathak, D. (2013). Privacy-Preserving Data Integration Using Decoupled Data, in Security & Privacy in Social Networks, Y. Altshuler, et al., Editors. 2013, Springer NY. p. 225-253.
Kum, H.-C., Krishnamurthy, A., Pathak, D., Reiter, M., and Ahalt, S. Secure Decoupled Linkage (SDLink) system for building a social genome. In IEEE International Conf on BigData. 2013.
