Jim Waldo

Privacy, anonymity, and big data in the social sciences

Open data has tremendous potential for science, but, in human subjects research, there is a tension between privacy and releasing high-quality open data. Federal law governing student privacy and the release of student records suggests that anonymizing student data protects student privacy. Guided by this standard, we de-identified and released a data set from 16 MOOCs (massive open online courses) from MITx and HarvardX on the edX platform. In this article, we show that these and other de-identification procedures necessitate changes to data sets that threaten replication and extension of baseline analyses. To balance student privacy and the benefits of open data, we suggest focusing on protecting privacy without anonymizing data by instead expanding policies that compel researchers to uphold the privacy of the subjects in open data sets. If we want to have high-quality social science research and also protect the privacy of human subjects, we must eventually have trust in researchers. Otherwise, we’ll always have the strict tradeoff between anonymity and science illustrated here.

How to de-identify your data

Big data is all the rage; using large data sets promises to give us new insights into questions that have been difficult or impossible to answer in the past. This is especially true in fields such as medicine and the social sciences, where large amounts of data can be gathered and mined to find insightful relationships among variables. Data in such fields involves humans, however, and thus raises issues of privacy that are not faced by fields such as physics or astronomy.

Piggybacking Robots: Human-Robot Overtrust in University Dormitory Security

Can overtrust in robots compromise physical security? We positioned a robot outside a secure-access student dormitory and made it ask passersby for access. . Individual participants were as likely to assist the robot in exiting the dormitory (40% assistance rate, 4/10 individuals) as in entering (19%, 3/16 individuals). Groups of people were more likely than individuals to assist the robot in entering (71%, 10/14 groups). When the robot was disguised as a food delivery agent for the fictional start-up Robot Grub, individuals were more likely to assist the robot in entering (76%, 16/21 individuals). Lastly, participants who identified the robot as a bomb threat demonstrated a trend toward assisting the robot (87%, 7/8 individuals, 6/7 groups). Thus, overtrust-the unfounded belief that the robot does not intend to deceive or carry risk-can represent a significant threat to physical security at a university dormitory.

A threat analysis of RFID passports

Do RFID passports make us vulnerable to identity theft?

The API performance contract

When you call functions in an API, you expect them to work correctly; sometimes this expectation is called a contract between the caller and the implementation. Callers also have performance expectations about these functions, and often the success of a software system depends on the API meeting these expectations. So there’s a performance contract as well as a correctness contract. The performance contract is usually implicit, often vague, and sometimes breached (by caller or implementation). How can this aspect of API design and documentation be improved?

Statistical Tradeoffs between Generalization and Suppression in the De-identification of Large-Scale Data Sets

Data sets containing private information about individuals must satisfy privacy standards before being publicly released. One such standard, k-anonymity, reduces the probability of the re-identification of individuals by requiring that rare combinations of personally-identifiable information be represented by at least k distinct individuals. Records that violate this standard must be altered, which can lead to significant distortion of the statistical properties of the data set. In this paper, we discuss improvements to two techniques used to achieve k-anonymity, generalization and suppression, that confer k-anonymity while better preserving the statistical properties of an educational data set taken from a massive online open course platform, edX.