In the United States, identity theft resulted in corporate and consumer losses of $56 billion dollars in 2005, with up to 35 percent of known identity thefts caused by corporate data breaches. Many states have responded by adopting “data breach disclosure laws” that require firms to notify consumers if their personal information has been lost or stolen. While the laws are expected to reduce identity theft, their effect has yet to be empirically measured. We use panel data from the U.S. Federal Trade Commission to estimate the impact of data breach disclosure laws on identity theft from 2002 to 2009. We find that adoption of data breach disclosure laws reduce identity theft caused by data breaches by 6.1 percent, on average.

Do Data Breaches Disclosure Laws Reduce Identity Theft

Analyzing Security Incidents Reported by The United States Computer Emergency Readiness Team

Modern software systems are deployed in sociotechnical settings, combining social entities (humans and organizations) with technical entities (software and devices). In such settings, on top of technical controls that implement security features of software, regulations specify how users should behave in security-critical situations. No matter how carefully the software is designed and how well regulations are enforced, such systems are subject to breaches due to social (user misuse) and technical (vulnerabilities in software) factors. Breach reports, often legally mandated, describe what went wrong during a breach and how the breach was remedied. However, breach reports are not formally investigated in current practice, leading to valuable lessons being lost regarding past failures. Our research aim is to aid security analysts and software developers in obtaining a set of legal, security, and privacy requirements, by developing a crowdsourcing methodology to extract knowledge from regulations and breach reports. We present Corba, a methodology that leverages human intelligence via crowdsourcing, and extracts requirements from textual artifacts in the form of regulatory norms. We evaluate Corba on the US healthcare regulations from the Health Insurance Portability and Accountability Act (HIPAA) and breach reports published by the US Department of Health and Human Services (HHS). Following this methodology, we have conducted a pilot and a final study on the Amazon Mechanical Turk crowdsourcing platform. Corba yields high quality responses from crowd workers, which we analyze to identify requirements for the purpose of complementing HIPAA regulations. We publish a curated dataset of the worker responses and identified requirements. The results show that the instructions and question formats presented to the crowd workers significantly affect the response quality regarding the identification of requirements. We have observed significant improvement from the pilot to the final study by revising the instructions and question formats. Other factors, such as worker types, breach types, or length of reports, do not have notable effect on the workers’ performance. Moreover, we discuss other potential improvements such as breach report restructuring and text highlighting with automated methods.

/pdf/corba-crowdsourcing-to-obtain-requirements-from-regulations-26iek665o0.pdf

Çorba: crowdsourcing to obtain requirements from regulations and breaches

Successful software projects require engaged collaborators interacting with each other across the entire development life-cycle. Unfortunately, for social coding platforms, e.g., GitHub, identifying a suitable collaborator to strengthen their ties and improve their engagement in the project is challenging, given that reliable information for collaborator identification is often not readily available. In this work, we propose and evaluate a collaborator recommendation tool - CoopFinder - to help developers find collaborators in a specific project based on similar interests related to co&#x2013;changed files. We design our Web prototype&#x2013;tool using visualization techniques. As a result of the preliminary user study, we observed that 95% of the participants had positive impressions of the tool. Furthermore, 75% of participants stated that they would use the tool in their daily lives or recommend it to others. Repository: https://github.com/kattiana/CoopFinder 

CoopFinder: Finding Collaborators Based on Co–Changed Files

Digital Nudges for Encouraging Developer Behaviors.

A repository of privacy incidents is essential for understanding the attributes of products and policies that lead to privacy incidents. We describe our vision for a novel privacy incidents database and our progress toward building a prototype. Key challenges in gathering such a database include bootstrapping and sustainability. We propose a semi-automated framework that can recognize privacy incidents and related information from various online sources such as news, blogs, and social media. The crux of our framework is an incident classifier that identifies whether a piece of text in natural language is related to a privacy incident or not. We curate a dataset consisting of 1324 news articles of which 543 articles are about one or more privacy incidents. We train the incident classifier on this dataset, considering a variety of feature engineering, feature selection, and classification techniques. We find that our incident classifier yields an F1 measure of 93.1%, which is about 12% higher than the keyword search-based baselines we adopt.

Learning a Privacy Incidents Database

To help users gain awareness of tools and features available in applications, recommender systems can automatically suggest useful tools. Such systems aim to present recommendations just like users would recommend tools to one another, but little is known about the nature of these user-to-user recommendations. This paper explores user-to-user recommendations through a study of 13 pairs of software users performing data analysis tasks. We found that users were more likely to adopt tools when they were receptive to the recommendation, but did not find the recommendations were any more likely to be effective when they contained other characteristics such as politeness, persuasiveness, or referred to observable tools. These findings suggest that, for example, automated systems should avoid recommending obscure and unfamiliar tools, but making recommendations politely is not a critical design goal.

How software users recommend tools to each other

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Learning a Privacy Incidents Database

How software users recommend tools to each other