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John Goodwin

dblp:02/4426 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 2 first-authorArtificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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.

Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%
Databases, data mining, and information retrieval
1 paper
Data integration and cleaning · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › qualitative reasoning
qualitative spatial reasoning
0.212015
Using Qualitative Spatial Logic for Validating Crowd-Sourced Geospatial Data · AAAI 2015
Knowledge, reasoning and agents › Knowledge representation and reasoning
spatial reasoning
0.212015
Using Qualitative Spatial Logic for Validating Crowd-Sourced Geospatial Data · AAAI 2015
Data integration and cleaning
entity matching
0.212015
Using Qualitative Spatial Logic for Validating Crowd-Sourced Geospatial Data · AAAI 2015
Data integration and cleaning › entity resolution
geospatial entity resolution
0.212015
Using Qualitative Spatial Logic for Validating Crowd-Sourced Geospatial Data · AAAI 2015
Knowledge, reasoning and agents › Knowledge representation and reasoning
description logic
0.112015
Using Qualitative Spatial Logic for Validating Crowd-Sourced Geospatial Data · AAAI 2015

Methods — techniques the papers use, named apart from their topics

truth maintenance · 0.4qualitative spatial logic · 0.4description logic · 0.4
YearPublicationVenuePosition
2015 Using Qualitative Spatial Logic for Validating Crowd-Sourced Geospatial Data
abstract
We describe a tool, MatchMaps, that generates sameAs and partOf matches between spatial objects (such as shops, shopping centres, etc.) in crowd-sourced and authoritative geospatial datasets. MatchMaps uses reasoning in qualitative spatial logic, description logic and truth maintenance techniques, to produce a consistent set of matches. We report the results of an initial evaluation of MatchMaps by experts from Ordnance Survey (Great Britain’s National Mapping Authority). In both the case studies considered, MatchMaps was able to correctly match spatial objects (high precision and recall) with minimal human intervention.
Heshan Du, Hai H. Nguyen, Natasha Alechina, Brian Logan 0001, Mike Jackson 0004, John Goodwin
AAAI6
2010 Power analysis detectable watermarks for protecting intellectual property
abstract
In this paper a method for adding a watermark to the power consumption of intellectual property is presented. The signature can be detected by using Differential Power Analysis, a cryptographic side channel attack that uses statistical techniques to detect register transitions in power consumption data. Details are given on how to calculate the probability of correctly determining whether the watermark is there. The number of samples that will be required to detect the watermark with a given accuracy can also be calculated based on how much of a price the designer is willing to pay in terms of area.
John Goodwin, Peter R. Wilson
ISCAS1
2008 Advanced Encryption Standard (AES) implementation with increased DPA resistance and low overhead
abstract
The use of the Advanced Encryption Standard (AES) has become pervasive in a large number of communication applications. The hardware to implement AES is non trivial and as a result it has a significant overhead in its raw form. Despite the algorithm being inherently secure, side channel attacks have exposed potential weaknesses via the implementation route, particularly power analysis. As a result, much effort has been expended in identifying techniques to mask the power signature that can give away the secret key. Unfortunately, the hardware overhead is usually a significant proportion of the size of the original algorithm and in addition, the key can sometimes still be ascertained due to manufacturing differences (such as track lengths) still delivering aspects of the key in a power signature. This paper describes an approach that requires a simple modification to the implementation of the AES algorithm, without altering its basic characteristics, which provides a significantly improved strength against side channel attacks with a minimal additional hardware overhead.
John Goodwin, Peter R. Wilson
ISCAS1