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  1. Proceedings of the 2016 ACM on Workshop on Information Sharing and Collaborative Security (WISCS '16)
  2. Privacy Risk in Cybersecurity Data Sharing
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Back to the Roots: Information Sharing Economics and What We Can Learn for Security
A Model for Secure and Mutually Beneficial Software Vulnerability Sharing
Private Sharing of IOCs and Sightings
Privacy Risk in Cybersecurity Data Sharing
Shall We Collaborate?: A Model to Analyse the Benefits of Information Sharing
Managing Data Sharing in OpenStack Swift with Over-Encryption
Data Quality Challenges and Future Research Directions in Threat Intelligence Sharing Practice
Collaborative Incident Handling Based on the Blackboard-Pattern
MISP: The Design and Implementation of a Collaborative Threat Intelligence Sharing Platform
Measuring the Impact of Sharing Abuse Data with Web Hosting Providers

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Privacy Risk in Cybersecurity Data Sharing

Content Provider ACM Digital Library
Author Friedberg, Liora Bhatia, Jaspreet Breaux, Travis D. Smullen, Daniel Hibshi, Hanan
Abstract As information systems become increasingly interdependent, there is an increased need to share cybersecurity data across government agencies and companies, and within and across industrial sectors. This sharing includes threat, vulnerability and incident reporting data, among other data. For cyberattacks that include sociotechnical vectors, such as phishing or watering hole attacks, this increased sharing could expose customer and employee personal data to increased privacy risk. In the US, privacy risk arises when the government voluntarily receives data from companies without meaningful consent from individuals, or without a lawful procedure that protects an individual's right to due process. In this paper, we describe a study to examine the trade-off between the need for potentially sensitive data, which we call incident data usage, and the perceived privacy risk of sharing that data with the government. The study is comprised of two parts: a data usage estimate built from a survey of 76 security professionals with mean eight years' experience; and a privacy risk estimate that measures privacy risk using an ordinal likelihood scale and nominal data types in factorial vignettes. The privacy risk estimate also factors in data purposes with different levels of societal benefit, including terrorism, imminent threat of death, economic harm, and loss of intellectual property. The results show which data types are high-usage, low-risk versus those that are low-usage, high-risk. We discuss the implications of these results and recommend future work to improve privacy when data must be shared despite the increased risk to privacy.
Starting Page 57
Ending Page 64
Page Count 8
File Format PDF
ISBN 9781450345651
DOI 10.1145/2994539.2994541
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-10-24
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Personal privacy Cybersecurity data sharing Risk perception Data usage
Content Type Text
Resource Type Article
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