Identity theft

About: Identity theft is a research topic. Over the lifetime, 2284 publications have been published within this topic receiving 31700 citations.

Addressing Identity Crime in Crime Management Information Systems: Definitions, Classification and Empirics

Abstract: Identity fraud as a term and concept in its formative stages was often presumed to be identity theft and visa versa. However, identity theft is caused by the identities (or tokens) of individuals or organisations being stolen is an enabling precursor to identity fraud. The boundaries of identity fraud and identity theft are now better defined. The absence of specific identity crime legislation could be a cause of perpetrators not classified as breaching identity crimes but under other specific entrenched law such as benefit fraud, or credit card fraud. This metrics overlap can cause bias in crime management information systems. This study uses a multi method approach where data was collected in both a quantitative and qualitative manner. These approaches are used as a lens for defining different classes of online identity crimes in a crime management (IS) security context. In doing so, we contribute to a deeper understanding of identity crime by specifically examining its hierarchical classes and definitions; to aid clearer structure in crime management IS. We seek to answer the questions:should current law around identity fraud continue to be reinforced and measures introduced to prevent identity crime; should laws be amended; or should new identity crime laws be constructed? We conclude and recommend a solution incorporating elements of all three.

Identity Theft Detection in Mobile Social Networks Using Behavioral Semantics

Abstract: User behavioral analysis is expected to be a key technique for identity theft detection in the Internet, especially in mobile social networks (MSNs). While traditional methods prefer to use explicit behaviors, a series of behaviors implicit in user's texts can probably provide much more accurate identity. And these implicit behaviors can be digged from texts by LDA. Besides the latent feature in texts, a behavior also include other features (e.g., spatial and temporal features). A joint feature including these features can be a better evidence for identity theft detection. In this paper, we use a probabilistic generative model to detect identity theft in MSNs. We are going to conduct experiments on two real-life datasets: Foursquare and Yelp. A early experiment shows that semantic features achieve better performance than spatial features and we are conducting our main experiment to see a better performance with joint behavioral feature.

The evolutional view of the types of identity thefts and online frauds in the era of the internet

Abstract: As far back as the early 1990s, the Internet was argued to be a unique medium showing the fastest speed of diffusion in human history (Nguyen and Alexander, 1996). Today, there are very few people whose lives are not affected beneficially and/or harmfully by the technology of the Internet era. On the positive side, the ability to share and exchange information instantaneously has provided unprecedented benefits in the areas of education, commerce, entertainment and social interaction. On the negative side, it has created increasing opportunities for the commission of crimes - information technology has enabled potential offenders to commit large-scale crimes with almost no monetary cost and much lesser risk of being caught. Compared to perpetrators of traditional economic- motivated crimes (e.g., burglaries, larcenies, bank robberies), online fraudsters are relatively free of worry from directly encountering law enforcement and witnesses. The authors aim to examine identity theft from an analytic angle with a focus on the expanded versatilities of this contemporary crime. In the present article, the mechanism of identifying an individual is first discussed, followed by the definition and typology of identity theft. Elements and methods of identity theft will be deconstructed for classification, and subsequent discussions will be emphasized on recent variations in online fraud. The study will conclude with the implications of the close relations between identity theft and the fast growing Internet, and suggestions for improved means of identity protection.

Predicting and explaining identity risk, exposure and cost using the ecosystem of identity attributes

Abstract: Personally Identifiable Information (PII) is commonly used in both the physical and cyber worlds to perform personal authentication. A 2014 Department of Justice report estimated that roughly 7% of American households reported some type of identity theft in the previous year, involving the theft and fraudulent use of such PII. Establishing a comprehensive map of PII attributes and their relationships is a fundamental first step to protect users from identity theft. In this paper, we present the mathematical representation and implementation of a model of Personally Identifiable Information attributes for people, named Identity Ecosystem. Each PII attribute (e.g., name, age, and Social Security Number) is modeled as a graph node. Probabilistic relationships between PII attributes are modeled as graph edges. We have implemented this Identity Ecosystem model as a Bayesian Belief Network (with cycles allowed) and we use Gibb's Sampling to approximate the posteriors in our model. We populated the model from two sources of information: 1) actual theft and fraud cases; and 2) experts' estimates. We have utilized our Identity Ecosystem implementation to predict as well as to explain the risk of losing PII and the liability associated with fraudulent use of these PII attributes. For better human understanding of the complex identity ecosystem, we also provide a 3D visualization of the Identity Ecosystem model and queries executed on the model. This research aims to advance a fundamental understanding of PII attributes and leads to better methods for preventing identity theft and fraud.