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Felix Ritchie

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Felix Ritchie

Professor in Economics


User-focused threat identification for anonymised microdata (2019)
Journal Article
Ritchie, F., Hafner, H., & Lenz, R. (2019). User-focused threat identification for anonymised microdata. Statistical Journal of the IAOS, 35(4), 703-713. https://doi.org/10.3233/SJI-190506

© 2019 - IOS Press and the authors. When producing anonymised microdata for research, national statistics institutes (NSIs) identify a number of 'risk scenarios' of how intruders might seek to attack a confidential dataset. This approach has been cri... Read More about User-focused threat identification for anonymised microdata.

Analyzing the disclosure risk of regression coefficients (2019)
Journal Article
Ritchie, F. (2019). Analyzing the disclosure risk of regression coefficients. Transactions on data privacy, 12(2), 145-173

A major growth area in social science research this century has been access to highly sensitive confidential microdata, often via restricted-access remote facilities. These allow researchers highly unlimited access to manipulate the data but with che... Read More about Analyzing the disclosure risk of regression coefficients.

The five safes of risk-based anonymization (2019)
Journal Article
Arbuckle, L., & Ritchie, F. (2019). The five safes of risk-based anonymization. IEEE Security and Privacy Magazine, 17(5), 84-89. https://doi.org/10.1109/MSEC.2019.2929282

The sharing of data for the purposes of data analysis and research can have many benefits. At the same time, concerns and controversies about data ownership and data privacy elicit significant debate. So how do we utilize data in a way that protects... Read More about The five safes of risk-based anonymization.

Confidentiality and linked data (2018)
Book Chapter
Ritchie, F., & Smith, J. Confidentiality and linked data. In G. Roarson (Ed.), Privacy and Data Confidentiality Methods – a National Statistician’s Quality Review, 1-34. Office for National Statistics

This chapter considers the confidentiality issues around linked data. It notes that the use and availability of secondary (adminstrative or social media) data, allied to powerful processing and machine learning techniques, in theory means that re-ide... Read More about Confidentiality and linked data.

Spontaneous recognition: An unneccessary control on data access? (2017)
Book Chapter
Ritchie, F. (2017). Spontaneous recognition: An unneccessary control on data access?. In G. Sandor, Z. Vereczkei, J. Poggi, P. Nymand-Andersen, A. Manninen, M. Karlberg, …C. Boldsen (Eds.), Selected papers from the 2016 Conference of European Statistics Stakeholders, 148-158. Publications Office of the European Union. https://doi.org/10.2785/091435

Social scientists increasingly expect to have access to detailed source microdata for research purposes. As the level of detail increases, data owners worry about ‘spontaneous recognition’, the likelihood that a microdata user believes that he or she... Read More about Spontaneous recognition: An unneccessary control on data access?.

Open data: Who needs it? (2017)
Presentation / Conference
Ritchie, F. (2017, September). Open data: Who needs it?. Presented at UNECE/Eurostat work session on statistical data confidentiality - 2017

This presentation, to introduce and stimulate a panel discussion, argued that we have 50 years worth of experience in knowing how to use data safely for researcher; as such we should be concentrating on practical management problems not theory: "how-... Read More about Open data: Who needs it?.

Lessons learned in training ‘safe users’ of confidential data (2017)
Presentation / Conference
Ritchie, F., Green, E., Newman, J., & Parker, T. (2017, September). Lessons learned in training ‘safe users’ of confidential data. Paper presented at UNECE/Eurostat work session on statistical data confidentiality - 2017

Many statistical organisations require researchers using detailed sensitive data to undergo ‘safe researcher’ training. Such training has traditionally reflected the ‘policing’ model of data protection. This mirrors the defensive stance often adopted... Read More about Lessons learned in training ‘safe users’ of confidential data.

The "Five Safes": A framework for planning, designing and evaluating data access solutions (2017)
Presentation / Conference
Ritchie, F. (2017, September). The "Five Safes": A framework for planning, designing and evaluating data access solutions. Paper presented at Data for Policy 2017, London, UK

The ‘Five Safes’ is a popular way to structure thinking about data access solutions. Originally used mainly by statistical agencies and social science academics , in recent years it has been adopted more widely across government, health organisations... Read More about The "Five Safes": A framework for planning, designing and evaluating data access solutions.

Spontaneous recognition: An unnecessary control on data access? (2017)
Journal Article
Ritchie, F. (2017). Spontaneous recognition: An unnecessary control on data access?. https://doi.org/10.2866/430525

Social scientists increasingly expect to have access to detailed data for research purposes. As the level of detail increases, data providers worry about “spontaneous recognition”, the likelihood that a microdata user believes that he or she has acci... Read More about Spontaneous recognition: An unnecessary control on data access?.

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