acm - an acm publication

2017 - May

  • 10 rules for an unhackable data vault

    Most recent publicity on cyber security has focused on preventing attacks by external hackers. While many of these attacks began with an insider, there has been much less discussion about preventing malicious insider exploits. Perhaps that is because untrustworthy insiders are hard to find and block before they strike. The Secure Data Vault (SDV) is an approach to protecting the most sensitive data from malware and insider exploits. Formal verification of the microservices that govern access to the vault will close down almost all malware pathways. The old military N-person rule will close down most insider pathways. This rule allows for a trade-off between security and convenience: the higher the number who have to cooperate to access the vault (N), the greater the security and the less the convenience. When based on this plus nine other construction rules, the SDV will protect sensitive data from malware and malicious insiders.

  • Cybersecurity skeptics now embracing formal methods: an interview with Gernot Heiser and Jim Morris

    There is new hope for those who despair securing computer systems from external hackers. The recent DARPA HACMS project demonstrated conclusively that "certain pathways for attackers have all been shut down in a way that's mathematically proven to be unhackable for those pathways." Continuing research at DARPA and IARPA will eventually shut down all the pathways, and the external hackers will be out of business permanently.

2018 Symposia

Ubiquity symposium is an organized debate around a proposition or point of view. It is a means to explore a complex issue from multiple perspectives. An early example of a symposium on teaching computer science appeared in Communications of the ACM (December 1989).

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Ubiquity Symposium: Big Data

Table of Contents

  1. Big Data, Digitization, and Social Change (Opening Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic
  2. Big Data and the Attention Economy by Bernardo A. Huberman
  3. Big Data for Social Science Research by Mark Birkin
  4. Technology and Business Challenges of Big Data in the Digital Economy by Dave Penkler
  5. High Performance Synthetic Information Environments: An integrating architecture in the age of pervasive data and computing By Christopher L. Barrett, Jeffery Johnson, and Madhav Marathe
  6. Developing an Open Source "Big Data" Cognitive Computing Platform by Michael Kowolenko and Mladen Vouk
  7. When Good Machine Learning Leads to Bad Cyber Security by Tegjyot Singh Sethi and Mehmed Kantardzic
  8. Corporate Security is a Big Data Problem by Louisa Saunier and Kemal Delic
  9. Big Data: Business, technology, education, and science by Jeffrey Johnson, Luca Tesei, Marco Piangerelli, Emanuela Merelli, Riccardo Paci, Nenad Stojanovic, Paulo Leitão, José Barbosa, and Marco Amador
  10. Big Data or Big Brother? That is the question now (Closing Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic