TY - GEN
T1 - On augmented identifying codes for monitoring drug trafficking organizations
AU - Basu, Kaustav
AU - Sen, Arunabha
PY - 2019/8/27
Y1 - 2019/8/27
N2 - A staggering 450,000 people died due to drug consumption in 2015, out of which, a third of the deaths were a direct result of drug overdosing. Illicit manufacturing of Cocaine, Heroin, Cannabis, etc., by Drug Trafficking Organizations (DTOs), all peaked recently, which is a major indication of their worldwide demand. With drug offenses increasing globally, the list of suspect individuals, associated with drug trafficking organizations, has also been growing over the past few decades. As it takes significant amount of technical and human resources to monitor a suspect, an increasing list entails greater resource requirements on the part of law enforcement agencies. Soon, monitoring all the suspects on the list becomes an impossible task. In this paper, we present a novel methodology called Augmented Identifying Codes (AIC), an extension of the mathematical notion of Identifying Codes. We show that our method requires significantly lesser resources, on the part of the law enforcement agencies, when compared to strategies adopting standard network centrality measures, for monitoring of individuals associated with drug trafficking organizations. Finally, we evaluate the efficacy of our approach on real world datasets.
AB - A staggering 450,000 people died due to drug consumption in 2015, out of which, a third of the deaths were a direct result of drug overdosing. Illicit manufacturing of Cocaine, Heroin, Cannabis, etc., by Drug Trafficking Organizations (DTOs), all peaked recently, which is a major indication of their worldwide demand. With drug offenses increasing globally, the list of suspect individuals, associated with drug trafficking organizations, has also been growing over the past few decades. As it takes significant amount of technical and human resources to monitor a suspect, an increasing list entails greater resource requirements on the part of law enforcement agencies. Soon, monitoring all the suspects on the list becomes an impossible task. In this paper, we present a novel methodology called Augmented Identifying Codes (AIC), an extension of the mathematical notion of Identifying Codes. We show that our method requires significantly lesser resources, on the part of the law enforcement agencies, when compared to strategies adopting standard network centrality measures, for monitoring of individuals associated with drug trafficking organizations. Finally, we evaluate the efficacy of our approach on real world datasets.
KW - Augmented identifying code
KW - Drug trafficking organizations
KW - Surveillance
UR - http://www.scopus.com/inward/record.url?scp=85078867294&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078867294&partnerID=8YFLogxK
U2 - 10.1145/3341161.3343530
DO - 10.1145/3341161.3343530
M3 - Conference contribution
T3 - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
SP - 1136
EP - 1139
BT - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
A2 - Spezzano, Francesca
A2 - Chen, Wei
A2 - Xiao, Xiaokui
PB - Association for Computing Machinery, Inc
T2 - 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
Y2 - 27 August 2019 through 30 August 2019
ER -