Sayler, Andy (University of Colorado at Boulder) "Securing Secrets and Managing Trust in Modern Computing Applications"(2016)

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

News and Resources for Members of the IEEE Signal Processing Society

Sayler, Andy (University of Colorado at Boulder) "Securing Secrets and Managing Trust in Modern Computing Applications"(2016)

Sayler, Andy (University of Colorado at Boulder) “Securing Secrets and Managing Trust in Modern Computing Applications” (2016) Advisor: Dirk Grunwald

The amount of digital data generated and stored by users increases every day. In order to protect this data, modern computing systems employ numerous cryptographic and access control solutions. Almost all of such solutions, however, require the keeping of certain secrets as the basis of their security models. How best to securely store and control access to these secrets is a significant challenge: such secrets must be stored in a manner that protects them from a variety of potentially malicious actors while still enabling the kinds of functionality users expect.
This dissertation discusses a system for isolating secrets from the applications that rely on them and storing these secrets via a standardized, service-oriented secret storage system. This “Secret Storage as a Service” (SSaaS) model allows users to reduce the trust they must place in any single actor while still providing mechanisms to support a range of cloud-based, multi-user, and multi-device use cases.

This dissertation contains the following contributions: an overview of the secret-storage problem and how it relates to the security and privacy of modern computing systems and users, a framework for evaluating the degree by which one must trust various actors across a range of popular use cases and the mechanisms by which this trust can be violated, a description of the SSaaS model and how it helps avoid such trust and security failures, a discussion of how the SSaaS approach can integrate with and improve the security of a range of applications, an overview of Custos – a first-generation SSaaS prototype, an overview of Tutamen – a next-generation SSaaS prototypes, and an exploration of the legal and policy implications of the SSaaS ecosystem.

SPS on Twitter

  • DEADLINE EXTENDED: The 2023 IEEE International Workshop on Machine Learning for Signal Processing is now accepting… https://t.co/NLH2u19a3y
  • ONE MONTH OUT! We are celebrating the inaugural SPS Day on 2 June, honoring the date the Society was established in… https://t.co/V6Z3wKGK1O
  • The new SPS Scholarship Program welcomes applications from students interested in pursuing signal processing educat… https://t.co/0aYPMDSWDj
  • CALL FOR PAPERS: The IEEE Journal of Selected Topics in Signal Processing is now seeking submissions for a Special… https://t.co/NPCGrSjQbh
  • Test your knowledge of signal processing history with our April trivia! Our 75th anniversary celebration continues:… https://t.co/4xal7voFER

IEEE SPS Educational Resources

IEEE SPS Resource Center

IEEE SPS YouTube Channel