Privacy in Research Data Managemnt - Use Cases


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From Integrating Approaches to Privacy across the Research Lifecycle

This workshop will consider how emerging tools and perspectives from a variety of disciplines, such as computer science, social science, law, and the health sciences, should be integrated in the management of confidential research data. Multidisciplinary discussion groups will grapple with these issues in the context of exemplar research use cases.

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  • This work. by Micah Altman ( is licensed under the Creative Commons Attribution-Share Alike 3.0 United States License. To view a copy of this license, visit or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.
  • The structure and design of digital storage systems is a cornerstone of digital preservation. To better understand ongoing storage practices of organizations committed to digital preservation, the National Digital Stewardship Alliance conducted a survey of member organizations. This talk discusses findings from this survey, common gaps, and trends in this area.(I also have a little fun highlighting the hidden assumptions underlying Amazon Glacier's reliability claims. For more on that see this earlier post: )
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  • Privacy in Research Data Managemnt - Use Cases

    1. 1. Prepared for: Integrating Approaches to Privacy across the Research Lifecycle Sept 2013 Introduction to Research Data Privacy Use Cases Micah Altman <> Director of Research, MIT Libraries Non-Resident Senior Fellow, Brookings Institution
    2. 2. DISCLAIMER These opinions are my own, they are not the opinions of MIT, Brookings, any of the project funders, nor (with the exception of co-authored previously published work) my collaborators. Secondary disclaimer: “It’s tough to make predictions, especially about the future!” -- Attributed to Woody Allen, Yogi Berra, Niels Bohr, Vint Cerf, Winston Churchill, Confucius, Disreali [sic], Freeman Dyson, Cecil B. Demille, Albert Einstein, Enrico Fermi, Edgar R. Fiedler, Bob Fourer, Sam Goldwyn, Allan Lamport, Groucho Marx, Dan Quayle, George Bernard Shaw, Casey Stengel, Will Rogers, M. Taub, Mark Twain, Kerr L. White, etc. Introduction to Research Data Privacy Use Cases
    3. 3. About the ‘use cases”? Technical definition: A summary of a pattern of interactions between external actors within a system under consideration to accomplish a goal. Working definition: Who does what, when; and what do they wish to accomplish? Complemented by: • User stories – simle generalized descriptions of specific interactions • Scenarios – variations on a theme • Examples/fact patterns – real life examples of the abstract use case Introduction to Research Data Privacy Use Cases
    4. 4. Data InputOutput Model Published Outputs * Jones * * 1961 021* * Jones * * 1961 021* * Jones * * 1972 9404* * Jones * * 1972 9404* * Jones * * 1972 9404* “The correlation between X and Y was large and statistically significant” Summary statistics Contingency table Public use sample microdata Information Visualization Introduction to Research Data Privacy Use Cases DATA DATA
    5. 5. Information Life Cycle Model Introduction to Research Data Privacy Use Cases Creation/Colle ction Storage/I ngest Processing Internal SharingAnalysis External dissemination/publica tion Re-use • Scientometric • Education • Scientific • Policy Long-term access Research methods Data Management Systems Legal / Policy Frameworks∂ ∂ Statistical / Computational Frameworks
    6. 6. Legal/Policy Frameworks Contract Intellectual Property Access Rights Confidentiality Copyright Fair Use DMCA Database Rights Moral Rights Intellectual Attribution Trade Secret Patent Trademark Common Rule 45 CFR 26 HIPAA FERPA EU Privacy Directive Privacy Torts (Invasion, Defamation) Rights of Publicity Sensitive but Unclassified Potentially Harmful (Archeological Sites, Endangered Species, Animal Testing, …) Classified FOIA CIPSEA State Privacy Laws EAR State FOI Laws Journal Replication Requirements Funder Open Access Contract License Click-Wrap TOU ITAR Export Restrictions
    7. 7. Introduction to Research Data Privacy Use Cases Example: Stakeholder Concerns Across Lifecycle Research sources: - Research Subjects. - Owners of subject material - Owners of supplementary data Research sponsors: - Home institution - Funding sources Project Personnel: - Investigators - Research Staff Research Publishers - Print publishers - Research archives Research Consumers - Readers - Secondary researcher Licensing Copyright DMCA Informed Consent Privacy Trade secrets Licensing Freedom of Information Copyright Copyright Copyright Licensing Fair Use Information Transfer Privacy Confidentiality Intellectual Property Replicable Research Policy Relevance Accessibility of Research Protect IP Avoid third party IP/Privacy Issues Replicable Research Publish Promote use of Publications Track use Replicable research Promote use of their publications Protect publisher IP Avoid third party IP/Privacy Issues Replicate and extend Secondary analysis Link research Stakeholder Concerns Legal Issues
    8. 8. • Infrastructure requirements analysis – Data acquisition, storage, dissemination – Identification, authorization, authentication – Metadata, protocols • System design: potential implementation cost of differential privacy: – Information security -- hardening – Information security – certification & auditing – Model server development, provisioning, maintenance, reliability, availability • System design: information security tradeoffs of Interactive privacy mechanisms: – Availability risks: denial of service attack – Availability/integrity risks: privacy budget exhaustion attacks – Integrity risks: modification of delivered results (e.g. man-in-the-middle attacks) – Secrecy/privacy: breach of authentication/authorization layer • System design: optimizing privacy & utility across lifecycle – When does limiting disclosive data collection dominate methods at the data analysis stage – When does restricted virtual data enclaves + public synthetic data dominate interactive mechanisms • System design: Information use/reuse – Support of scientific analysis use cases (model diagnostics, exploratory data analysis, integration of externa data) within interactive privacy systems. – Align informational assumptions across stages & incorporating informative priors? – Requirements for scientific replication/verification of results produced by model servers? Introduction to Research Data Privacy Use Cases Systems Policy Research questions deriving from Information Lifecycle Analysis
    9. 9. Modeling Features Features Characteristics Data - Structure; Source; Unit of observation; Attribute types; Dimensionality; Number of observations; homogeneity; frequency of updates; quality characteristics Analytic Results - Form of output; analysis methodology; analysis/inferential goal; utility/loss/quality Disclosure scenario - - Source of threat; areas of vulnerability; attacker objectives, background knowledge, capability; Breach criteria/disclosure concept Stakeholders - Stakeholder types; capacities; trust relationships; budgets Lifecycle characteristics - Lifecycle stages controlled/in scope; policies used; stakeholders involved at each stage Current privacy management approach - Regulation/policy; legal controls; statistical/computational disclosure methods; information security controls Introduction to Research Data Privacy Use Cases
    10. 10. Exemplar: Social Media Analysis Introduction to Research Data Privacy Use Cases Attribute Type Examples Data: Structure - network Data: Attribute Types - Continuous/Discrete/ - Scale: ratio/interval/ordinal/nominal Data: Performance Characteristics - 10M-1B observations - Sample from stream of continuously updated corpus - Dozens of dimensions/measures Measurement: Unit of Observation - Individuals; Interactions Measurement: Measurement type - Observational Measurement: Performance characteristic - High volume - Complex network structure - Sparsity - Systematic and sparse metadata Management Constraints - License; Replication Analysis methods - Bespoke algorithms (clustering); nonlinear optimization; Bayesian methods Desired Outputs - Summary scalars (model coefficients) - Summary table - Static /interactive visualization More Information • Grimmer, Justin, and Gary King. "General purpose computer- assisted clustering and conceptualization." Proceedings of the National Academy of Sciences 108.7 (2011): 2643-2650. • King, Gary, Jennifer Pan, and Molly Roberts. "How censorship in China allows government criticism but silences collective expression." APSA 2012 Annual Meeting Paper. 2012. • Lazer, David, et al. "Life in the network: the coming age of computational social science." Science (New York, NY) 323.5915 (2009): 721.
    11. 11. Mapping the “Space” of Research Data Privacy • Many different types of potentially relevant features • Many types stakeholders • Many lifecycle stages  so can’t be exhaustive Heuristic: Choose some points -- combinations of characteristics -- that are near various corners of the (hyper-) space and that represent substantively important examples. Document these… Discuss. Think. Repeat. Introduction to Research Data Privacy Use Cases
    12. 12. ExampleUseCases Name/Description Examples Comparison case: Official Statistics Well-resourced data collector summarizes tables/relational data in the form of summary statistics and contingency tables • U.S. Census dissemination • European statistical agencies Privacy-Aware Journal Replication Policies Scholarly journals adopting policies for deposit and disposition of data for verification and replication. How to balance privacy and replicability without intensive review? • Data Sharing Systems for Open Access Journals • American Political Science Association Data Access and Research Transparency [DART] Policy Initiative Long-term Longitudinal data Collection Data collections tracking individual subjects (and possibly friends and relations) over decades • National Longitudinal Study of Adolescent Health (Add Health) • Framingham Heart Study • Panel Study of Income Dynamics Computational Social Science “Big” data. New forms and sources of data. Cutting-edge analytical methods and algorithms. Analyzing … • Netflix • Facebook • Hubway • GPS • Blogs Introduction to Research Data Privacy Use Cases
    13. 13. Proposed Discussion Questions (for tomorrow) • Characterization. • Current approaches. • Enhancing approaches. • Integrating approaches. • Utility. • Privacy. • Methodological Barriers • Incentives. • Future. • Prior work. Introduction to Research Data Privacy Use Cases • Are these summaries useful as descriptive models? • What is missing from the big picture? • What are the opportunities for research, practice & policy? (What one wants to know)(What one asks)
    14. 14. Selected Bibliography • L. Willenborg and T. D. Waal. Elements of Statistical Disclosure Control, volume 155 of Lecture Notes in Statistics. Springer Verlag, New York, NY, 2001. • Higgins, Sarah. "The DCC curation lifecycle model." International Journal of Digital Curation 3.1 (2008): 134- • ESSNET, Handbook on Statistical Disclosure Control. 2011. • Fung, Benjamin, et al. "Privacy-preserving data publishing: A survey of recent developments." ACM Computing Surveys (CSUR) 42.4 (2010): 14. • Altman, M. (2012). “Mitigating Threats To Data Quality Throughout the Curation Lifecycle. In G. Marciano, C. Lee, & H. Bowden (Eds.), Curating For Quality. Introduction to Research Data Privacy Use Cases
    15. 15. Questions? E-mail: Web: Twitter: @drmaltman Introduction to Research Data Privacy Use Cases
    16. 16. Appendix: Full Questions • Characterization. – Are there key additional characteristics of the use case that should be noted? How do these characteristics change the analysis and treatment of privacy in these cases? • Current approaches. – How is this use case treated now -- what's the state of the art & practice? How is success measured? • Enhancing approaches. – Are any of the approaches discussed yesterday used? How could the tools and approaches mentioned earlier or other existing tools be used at particular stages of the research lifecycle to enhance utility and privacy? • Integrating approaches. – Are approaches that have been developed and used in different communities compatible with each other? How should legal, computational, policy, and statistical tools be integrated so as to be most effective? • Utility. – What things would stakeholders like to do with the data that the toolset doesn't restrict or obstruct? Where is social benefit sub-optimal? How is utility measured/perceived by the stakeholders? • Privacy. – What sorts of data/outputs are considered particularly sensitive? What are the most important real and perceived risks -- what harms could occur if data is released and reidentified, how severe are these harms and how likely? • Methodological Barriers – . What are technical, methodological, computational or infrastructural barriers to improving privacy and utility in the management of this data. What particular characteristics of the use case contribute barriers? • Incentives. – If better tools already exist, why aren't they used? What are barriers to adoption of new tools and methods? What are the specific "market failures" in this area -- such as perverse incentives, lack/asymmetry of information, lack of well-developed market, irrational behavior, transaction cost, network effects, etc.? What particular characteristics of the use case most influence incentives? • Future. – How is this use case likely to evolve over time? What are threats to stability/scalability/robustness/resilience of the proposed/current solutions? • Prior work. – Are there key additional examples of the use case that should be noted? Are there additional key references or writings that should be noted? Introduction to Research Data Privacy Use Cases
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