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

https://www.sciendo.com/pdf/10.2478/popets-2021-0081

Unifying Privacy Policy Detection

Narrative generation systems can be classified on a spectrum from strong autonomy to strong story. Systems on the strong autonomy side treat characters as fully independent agents but may struggle to meet the author’s requirements, while those on the strong story side direct character behaviors centrally but may struggle to create the illusion of character believability. In this paper, we use benchmark story generation problems as a framework to compare the spaces of stories that could be generated by prototypical strong story and strong autonomy systems. Comparing the relative solution densities of these spaces helps us quantify how common certain desirable narrative properties are. This can be informative for system designers when deciding, for instance, whether to strictly enforce all desired properties or to generate and filter from a broader class of solutions.

A Good Story Is One in a Million: Solution Density in Narrative Generation Problems

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

Previous approaches to narrative generation have required a new planner implementation for each set of constraints deemed relevant to the narrative domain, each consisting of thousands of lines of code and supporting one primary mode of interaction: fully specifying a domain and problem, and receiving a plan as output. We present a lightweight, flexible narrative planner written with Answer Set Programming, designed specifically to support constraint-based narrative generation, show how it generalizes previous approaches, and show how it can be easily extended with notions of thematic plot schema such as “betrayal.” Finally, we demonstrate how the ASP model can be explored through interactive question answering, where answers take the form of generated narratives. In the long term, we intend this work to support understanding of complex rule systems through interactive exploration.

/pdf/generating-explorable-narrative-spaces-with-answer-set-nke5kwqcgt.pdf

Generating Explorable Narrative Spaces with Answer Set Programming

Informed consent has become increasingly salient for data privacy and its regulation. Entities from governments to for-profit companies have addressed concerns about data privacy with policies that enumerate the conditions for personal data storage and transfer. However, increased enumeration of and transparency in data privacy policies has not improved end-users’ comprehension of how their data might be used: not only are privacy policies written in legal language that users may struggle to understand, but elements of these policies may compose in such a way that the consequences of the policy are not immediately apparent. We present a framework that uses Answer Set Programming (ASP) — a type of logic programming — to formalize privacy policies. Privacy policies thus become constraints on a narrative planning space, allowing end-users to forward-simulate possible consequences of the policy in terms of actors having roles and taking actions in a domain . We demonstrate through the example of the Health Insurance Portability and Accountability Act (HIPAA) how to use the system in various ways, including asking questions about possibilities and identifying which clauses of the law are broken by a given sequence of events.

pdf/exploring-consequences-of-privacy-policies-with-narrative-2ievaliq.pdf

Exploring Consequences of Privacy Policies with Narrative Generation via Answer Set Programming

Expert-crafted puzzle games are notable for their ability to wordlessly teach players deep and interesting consequences of mechanics. Cognitive science theories suggest that this learning happens through an iterative process of hypothesizing, failure, and revision of mental models. In this work, we pose the question of what it would take to generate puzzle game levels and progressions that best support player satisfaction and systematic understanding of mechanics by integrating an understanding of player mental models. We present a preliminary investigation consisting of a new puzzle game Laserverse, whose mechanics are designed to interact with each other in a wide array of combinations, a set of handauthored levels that combine these mechanics hierarchically, and an algorithm for organizing the levels into progressions to test hypotheses based on a theory of mental model formation. This work sets the stage for empirical validation of puzzle game design principles, which in turn inform a new approach to player modeling based on mental model matching theory.

/pdf/generating-puzzle-progressions-to-study-mental-model-1kgu1z893f.pdf

Generating Puzzle Progressions to Study Mental Model Matching.

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Learning a Privacy Incidents Database

Generating Explorable Narrative Spaces with Answer Set Programming

Exploring Consequences of Privacy Policies with Narrative Generation via Answer Set Programming

Generating Puzzle Progressions to Study Mental Model Matching.