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There are two strategic and longstanding questions about cyber risk that organizations largely have been unable to answer: What is an organization's estimated risk exposure and how does its security compare with peers? Answering both requires industry-wide data on security posture, incidents, and losses that, until recently, have been too sensitive for organizations to share. Now, privacy enhancing technologies (PETs) such as cryptographic computing can enable the secure computation of aggregate cyber risk metrics from a peer group of organizations while leaving sensitive input data undisclosed. As these new aggregate data become available, analysts need ways to integrate them into cyber risk models that can produce more reliable risk assessments and allow comparison to a peer group. This paper proposes a new framework for benchmarking cyber posture against peers and estimating cyber risk within specific economic sectors using the new variables emerging from secure computations. We introduce a new top-line variable called the Defense Gap Index representing the weighted security gap between an organization and its peers that can be used to forecast an organization's own security risk based on historical industry data. We apply this approach in a specific sector using data collected from 25 large firms, in partnership with an industry ISAO, to build an industry risk model and provide tools back to participants to estimate their own risk exposure and privately compare their security posture with their peers.
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This content will become publicly available on July 14, 2026
Implications of Disability Definition, Determination, and Data Collection
Demographic data on disability is being collected more and more by computing organizations. However, the meaning of that data can vary drastically depending on the ways that disability is defined (e.g., disabling conditions versus functional limitations) and determined (e.g., by experts or self-reported). In this paper we analyze the varying definitions and determinations of disability and how data about disability is collected by different organizations. We discuss issues associated with certain ways of collecting disability data and offer ways that disability data can be used to inform action towards more inclusive computing education.
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- Award ID(s):
- 2417014
- PAR ID:
- 10627640
- Publisher / Repository:
- Proceedings of the 2025 Conference on Research on Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT 2025)
- Date Published:
- ISSN:
- xxxx-xxxx
- ISBN:
- 979-8-4007-0626-4
- Page Range / eLocation ID:
- 65-71
- Format(s):
- Medium: X
- Location:
- https://dl.acm.org/doi/10.1145/3704637.3734735
- Sponsoring Org:
- National Science Foundation
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