Demo Data from VLDB 2026 / reviewers in the wild / expert
Kyle Kauffman
dblp:35/9582
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 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.
| Computer networks
1 paper |
Internet of things and sensor networks · 100% | |
| Network and information security
1 paper |
Network security · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet of things and sensor networks › wireless sensor network
in-network aggregation |
0.1 | 1 | 2011 | General secure sensor aggregation in the presence of malicious nodes · ICDE 2011 |
Internet of things and sensor networks › wireless sensor network › data aggregation
secure data aggregation |
0.1 | 1 | 2011 | General secure sensor aggregation in the presence of malicious nodes · ICDE 2011 |
Network security › wireless network security
malicious node resilience |
0.0 | 1 | 2011 | General secure sensor aggregation in the presence of malicious nodes · ICDE 2011 |
Methods — techniques the papers use, named apart from their topics
secure multiparty computation · 0.2aggregation tree to aggregation list conversion · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | General secure sensor aggregation in the presence of malicious nodesabstractSensor networks have the potential to allow users to “query the physical world” by querying the sensor nodes for an aggregate result. However, a concern with such aggregation is that a few corrupt nodes in the network may manipulate the results that the querier sees. There has been a substantial amount of work on providing integrity for sensor network computations. However to the best of our knowledge, this prior work has one or more of the following two limitations: i) the methods only work for a specific aggregation function, or ii) the methods do not consider adversaries whose goal is to prevent the base station and the querier from receiving the result. In this paper we present the first scheme that provides general aggregation for sensor networks in the presence of malicious adversaries. The generality of the scheme results from the ability to securely evaluate any algorithm in the streaming model of computation. The main idea of this paper is to convert the commonly used aggregation tree into an aggregation list, and a process for doing this conversion is also presented. This result is an interesting and important first step towards achieving the realization of general secure querying of a sensor network. Keith B. Frikken, Kyle Kauffman, Aaron Steele |
ICDE | 2 |