Demo Data from EDBT 2026 / reviewers in the wild / expert
Mike Williamson
dblp:19/596
· DBLP profile ↗
5ranked-venue papers
2as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-authorDatabases, data management, data science and information retrieval · 1Theory of computation · 1Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Authentication and access control · 77% Usable security · 23% | |
| Artificial intelligence
4 papers |
Planning, search and constraint satisfaction · 49% Multi-agent systems · 29% Knowledge representation and reasoning · 22% |
Topics — the 5 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Authentication and access control › identity management
account recovery |
0.2 | 1 | 2015 | Secrets, Lies, and Account Recovery: Lessons from the Use of Personal Knowledge Questions at Google · WWW 2015 |
Knowledge, reasoning and agents › Multi-agent systems › trading agents
broker agents |
0.0 | 1 | 1997 | Middle-Agents for the Internet · IJCAI (1) 1997 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
temporal reasoning |
0.0 | 1 | 1993 | Exploiting Domain structure to Achieve Efficient Temporal Reasoning · IJCAI 1993 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning under uncertainty
planning under incomplete information |
0.0 | 1 | 1992 | An Approach to Planning with Incomplete Information · KR 1992 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
planning under uncertainty |
0.0 | 1 | 1992 | An Approach to Planning with Incomplete Information · KR 1992 |
Methods — techniques the papers use, named apart from their topics
user survey · 0.2large-scale data analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Secrets, Lies, and Account Recovery: Lessons from the Use of Personal Knowledge Questions at GoogleabstractWe examine the first large real-world data set on personal knowledge question's security and memorability from their deployment at Google. Our analysis confirms that secret questions generally offer a security level that is far lower than user-chosen passwords. It turns out to be even lower than proxies such as the real distribution of surnames in the population would indicate. Surprisingly, we found that a significant cause of this insecurity is that users often don't answer truthfully. A user survey we conducted revealed that a significant fraction of users (37%) who admitted to providing fake answers did so in an attempt to make them "harder to guess" although on aggregate this behavior had the opposite effect as people "harden" their answers in the same and predictable way. On the usability side, we show that secret answers have surprisingly poor memorability despite the assumption that their reliability motivates their continued deployment. From millions of account recovery attempts we observed a significant fraction of users (e.g 40% of our English-speaking US users) were unable to recall their answers when needed. This is lower than the success rate of alternative recovery mechanisms such as SMS reset codes (over 80%). Comparing question strength and memorability reveals that the questions that are potentially the most secure (e.g what is your first phone number) are also the ones with the worst memorability. We conclude that it appears next to impossible to find secret questions that are both secure and memorable. Secret questions continue have some use when combined with other signals, but they should not be used alone and best practice should favor more reliable alternatives. Joseph Bonneau, Elie Bursztein, Ilan Caron, Rob Jackson, Mike Williamson |
WWW | 5 |
| 1997 | Middle-Agents for the Internet
Keith S. Decker, Katia P. Sycara, Mike Williamson |
IJCAI (1) | 3 |
| 1994 | Utility-Directed Planning
Mike Williamson, Steve Hanks |
AAAI | 1 |
| 1993 | Exploiting Domain structure to Achieve Efficient Temporal Reasoning
Mike Williamson, Steve Hanks |
IJCAI | 1 |
| 1992 | An Approach to Planning with Incomplete Information
Oren Etzioni, Steve Hanks, Daniel S. Weld, Denise Draper, Neal Lesh, Mike Williamson |
KR | 6 |