Spend Summer 2015 at Harvard performing cutting-edge research on data privacy. The Privacy Tools Project (http://privacytools.seas.harvard.edu/) is looking for summer students in Computer Science, Statistics, Government, Mathematics, Law, and Social Sciences with Quantitative Experience, particularly those with an interest in learning about or working on Data Privacy.
-- Undergraduates should apply to our REU program at http://www.seas.harvard.edu/k-12-community-programs/reu by February 28.
-- Others (such as potential law interns, graduate students, postdocs, and visiting scholars interested in short or long-term opportunities) should email a cover letter, CV, and contact information for 2-3 references to firstname.lastname@example.org as soon as possible.
We are working on ways for scientists to share research data for producing replicable, open science, without compromising the privacy of the individual research subjects whose data is used. Students last summer wrote or contributed to publishable research papers in this fast-moving field, and we expect the same this year. The work across the different projects includes elements such as:
- -- Theory: proving mathematical theorems about what is achievable in the framework of “differential privacy,” which is a very active area of research in theoretical computer science and other fields,
- -- Experimental algorithms: implementing, optimizing, and testing algorithms that perform useful data analysis tasks and satisfy “differential privacy” or other privacy metrics,
- -- Empirical research: surveying social science datasets and analysis methods to determine the fit with different privacy technologies,
- -- Software development: including for statistics, user interfaces, and data visualization.
- -- Programming languages and computer security: designing and implementing programming language tools to ensure differential privacy and combining it with other computer security models.
- -- Law: developing legal instruments and policy recommendations that complement new privacy-preserving technologies.
- -- Interdisciplinary interaction: collaborating with computer scientists, social scientists, lawyers, and statisticians.
Useful background includes any of the following:
- -- Theoretical computer science, especially algorithms
- -- Data science, e.g. statistics and/or machine learning
- -- Quantitative analysis of social science data, especially regression ("least squares", or OLS)
- -- User interfaces and user experience testing
- -- Programming language design and implementation
- -- Law, especially privacy law