Security: Computing in an Adversarial Environment

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Thursday, April 12, 2012 at 2:00 PM EDT/1:00 PM CDT/11:00 AM PDTSecurity is inherently different from other aspects of computing due to the presence of an adversary. As a result, identifying and addressing security vulnerabilities requires a different mindset from traditional engineering. Proper security engineering—or the lack of it!—affects everything from website scripts to supply chain management to electronic health records to social networks to mobile phones…and the list goes on. Security is further complicated by the translation of social notions—such as identity and trust— into an online world. Worse, security itself is often viewed by both developers and users as the adversary! This learning webinar will introduce the fundamentals of security, describe the security mindset, and highlight why achieving security is difficult.

What you’ll learn:

  • The security mindset – what it is, why it’s needed
  • The social side of security – usability, adoption, identity, trust
  • A deeper dive on insider threat as a case study – what it is, how to detect it, how to prevent it
Presenter:
Carrie Gates
Senior Vice President and Director of Research, CA Labs
Dr. Gates has opened new avenues for collaboration in the field of cyber security for CA Technologies by leveraging government programs that further research between CA Labs and academia. She has given over 20 invited talks internationally, authored more than 40 peer-reviewed publications related to information security, and co-authored an amendment on cloud security research for the America Competes Act that was signed into law in December 2010. In October 2010, Dr. Gates was recognized for her work with a Women of Influence award from CSO magazine.Moderator:
Christopher W. CliftonAssociate Professor of Computer Science, Purdue University
Dr. Clifton works on data privacy, particularly with respect to analysis of private data. This includes privacy-preserving data mining, data de-identification and anonymization, and limits on identifying individuals from data mining models. He also works more broadly in data mining, including data mining of text and data mining techniques applied to interoperation of heterogeneous information sources. Christopher also works on database support for widely distributed and autonomously controlled information, particularly issues related to data privacy. Prior to joining Purdue in 2001, Dr. Clifton was a principal scientist in the Information Technology Division at the MITRE Corporation. Before joining MITRE in 1995, he was an assistant professor of computer science at Northwestern University.
Attendance for this webinar is free. Space is limited.This webcast provided by:

 

http://learning.acm.org/webinar/current

Managing Technical Debt

Managing Technical Debt

Shortcuts that save money and time today can cost you down the road.

ERIC ALLMAN

In 1992, Ward Cunningham published a report at OOPSLA (Object-oriented Programming, Systems, Languages, and Applications)2 in which he proposed the concept of technical debt. He defines it in terms of immature code: “Shipping first-time code is like going into debt.” Technical debt isn’t limited to first-time code, however. There are many ways and reasons (not all bad) to take on technical debt.

http://queue.acm.org/detail.cfm?id=2168798

Interactive Dynamics for Visual Analysis

A taxonomy of tools that support the fluent and flexible use of visualizations

JEFFREY HEER, STANFORD UNIVERSITY

BEN SHNEIDERMAN, UNIVERSITY OF MARYLAND, COLLEGE PARK

The increasing scale and availability of digital data provides an extraordinary resource for informing public policy, scientific discovery, business strategy, and even our personal lives. To get the most out of such data, however, users must be able to make sense of it: to pursue questions, uncover patterns of interest, and identify (and potentially correct) errors. In concert with data-management systems and statistical algorithms, analysis requires contextualized human judgments regarding the domain-specific significance of the clusters, trends, and outliers discovered in data.

http://queue.acm.org/detail.cfm?id=2146416

      Related:

A Conversation with Jeff Heer, Martin Wattenberg, and Fernanda Viégas

A Tour through the Visualization Zoo

A Conversation with Ed Catmull