Courses & Educational Materials

Course Description: This course focuses on information as quantity, resource, and property. We study the application of quantitative methods to understanding how information technologies inform issues of public policy, regulation, and law. How are music, images, and telephone conversations represented digitally, and how are they moved reliably from place to place through wires, glass fibers, and the air? Who owns information, who owns software, what forms of regulation and law restrict the communication and use of information, and does it matter? How can personal privacy be protected at the same time that society benefits from communicated or shared information?

The recorded lectures are from the Harvard Faculty of Arts and Sciences course Quantitative Reasoning 48, which was offered as an online course at the Extension School. The Quicktime and MP3 formats are available for download, or you can play the Flash version directly.

Course Materials: View all lectures related to privacy

If you are a practicing social scientist, Micah Altman's course "Managing Confidential Data" may be on interest. Below is information on the course, including slides.

Course: "Managing Confidential Data”"

Professor: Micah Altman

Course Description: This tutorial provides a framework for identifying and managing confidential information in research. It is most appropriate for mid-late career graduate students, faculty, and professional research staff who actively engage in the design/planning of research. The course will provide an overview of the major legal requirements governing confidential research data; and the core technological measures used to safeguard data. And it will provide an introduction to the statistical methods and software tools used to analyze and limit disclosure risks. Failures of confidentiality threaten research integrity, reputation, legality, and funding. Every researcher in the social, behavioral and health sciences must understand how to manage confidential information in research. Successful management of confidential information is particularly challenging because it requires satisfying a combination of complex legal, statistical and technological constants. And the management of this information has grown increasingly challenging because of recent changes in the law, new forms of data collection, and advances in statistical methods for linking data. Course materials for "Managing Confidential Information" are available here.

This course was offered twice in 2015 and once so far in 2016.

Course Website: http://informatics.mit.edu/classes/managingconfidentialdata

Course Materials: http://www.slideshare.net/slideshow/embed_code/21164359

Below is a tutorial video of co-PI Micah Altman presenting "Managing Confidential Data," from our Summer Interns' Orientation (Summer 2015)

The syllabus, readings, and homework assignments are available on the course website.

The syllabus, reading list, and homework assignments are available on the course website.

Course: CS 208: Applied Privacy for Data Science (Spring 2019)

Instructors: James Honaker and Salil Vadhan

The syllabus, reading list, and homework assignments are available on the course website

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• A popular article on differential privacy, aimed at the general public.

Ori Heffetz and Katrina Ligett, Privacy and Data-Based Research

• A survey about the need to preserve privacy when sharing research datasets, failed attempts at data anonymization, and differential privacy as a solution.  Aimed at practicing social scientists, and requires almost no mathematical knowledge at all.

Kobbi Nissim, Thomas Steinke, Alexandra Wood, Mark Bun, Marco Gaboardi, David R. O'Brien, and Salil Vadhan, Differential Privacy: A Primer for a Non-technical Audience (Preliminary Version)

• This document is a primer on differential privacy, which is a formal mathematical framework for guaranteeing privacy protection when analyzing or releasing statistical data. Recently emerging from the theoretical computer science literature, differential privacy is now in initial stages of implementation and use in various academic, industry, and government settings. Using intuitive illustrations and limited mathematical formalism, this document provides an introduction to differential privacy for non-technical practitioners, who are increasingly tasked with making decisions with respect to differential privacy as it grows more widespread in use. In particular, the examples in this document illustrate ways in which social scientists can conceptualize the guarantees provided by differential privacy with respect to the decisions they make when managing personal data about research subjects and informing them about the privacy protection they will be afforded. 

Daniel Muise and Kobbi Nissim, Differential Privacy in CDFs

• This presentation familiarizes social scientists with the errors introduced by differential privacy (DP), and explains how to manage DP’s random noise. It explains the effect of random noise introduced in DP-computations by making analogies to sampling error, and focuses on the case of cumulative density functions (CDFs) and histograms.

Collaborator Cynthia Dwork, A Firm Foundation for Private Data Analysis

• A survey article on differential privacy, aimed at a broad computer science audience, with some mathematical background.

• A comprehensive book about the theory of differential privacy, suitable for graduate students or advanced undergraduates in computer science or other mathematical sciences. Even non-CS audience would appreciate the first two episodes, which overlap greatly with the others surveys below.

• This tutorial provides an introduction to and overview of differential privacy, with the goal of conveying its deep connections to a variety of other topics in computational complexity, cryptography, and theoretical computer science at large.

• A survey of the many connections between differential privacy and economic mechanism design, aimed at mathematically oriented readers having some familiarity with game theory and mechanism design.

• PI Salil Vadhan’s NSF WATCH talk on “Differential Privacy: Theoretical and Practical Challenges," January 2015 (you will need to register to view the video)
• Tutorials from DIMACS Workshop on Recent Work on Differential Privacy across Computer Science, October 2012.  There are tutorials on work in differential privacy relating to the theoretical computer science, databases, programming languages, and algorithmic game theory communities
• Tutorial on differential privacy by Katrina Ligett at the Simons Institute workshop on Big Data and Differential Privacy, December 2013.

Professor: Salil P. Vadhan

Course Description: Algorithms to guarantee privacy and authenticity of data during communication and computation. Rigorous proofs of security based on precise definitions and assumptions. Topics may include one-way functions, private-key and public-key encryption, digital signatures, pseudorandom generators, fully homomorphic encryption, and the role of cryptography in network and systems security.

Fall 2013 and fall 2006 lecture notes, videos, and homework assignments are available on the course website.