Identity theft

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

An Evaluation of Privacy and Security Issues at a Small University

Abstract: Colleges and universities process large amounts of personal information obtained from employees, students, and the general public. Such information includes income tax returns, employment history, salary, loans, and credit information provided by students and their parents. Additionally, data gathered from research, admissions records, medical files, and library access information are also maintained. Institutions of higher learning represent a large portion of the United States’ network and computing infrastructure, which accounts for approximately 15 percent of all Internet domains [15]. Colleges and universities are major targets for identity theft because of the sizable amount of data that is managed and stored within these institutions. The copious amounts of information that colleges and universities maintain suggest that security should be a top priority. How can higher education institutions protect personal and sensitive information of their students and employees? Most institutions focus on privacy and security for students; however, their employee’s personal information is just as vulnerable to security invasions. This study will explore how a small university can decrease privacy concerns and security attacks against its students and employees.

On Complementary Effect of Blended Behavioral Analysis for Identity Theft Detection in Mobile Social Networks

Abstract: User behavioral analysis is expected to act as a promising technique for identity theft detection in the Internet. The performance of this paradigm extremely depends on a good individual-level user behavioral model. Such a good model for a specific behavior is often hard to obtain due to the insufficiency of data for this behavior. The insufficiency of specific data is mainly led by the prevalent sparsity of users’ collectable behavioral footprints. This work aims to address whether it is feasible to effectively detect identify thefts by jointly using multiple unreliable behavioral models from sparse individual-level records. We focus on this issue in mobile social networks (MSNs) with multiple dimensions of collectable but sparse data of user behavior, i.e., making check-ins, posing tips and forming friendships. Based on these sparse data, we build user spatial distribution model, user post interest model and user social preference model, respectively. Here, as the arguments, we validate that there is indeed a complementary effect in multi-dimensional blended behavioral analysis for identity theft detection in MSNs.

The Future of Privacy Policies: A Privacy Nutrition Label Filled with Fair Information Practices

Abstract: E-commerce continues to blossom as evidenced by online retail sales in excess of $33 billion over the first quarter 2008. This growth helps spur the staggering economy but also magnifies the serious threats surrounding personally identifying information (PII) submitted during e-commerce transactions. The most common threats, such as identity theft and aggregated data files, do the most damage when companies are careless (i.e., losing laptops filled with unencrypted data) or callous (selling data on the open market) with the PII they collect. The first line of defense against these threats is the electronic privacy policy. In theory, privacy policies are supposed to force companies to analyze and strengthen their privacy practices and then provide Web surfers with a detailed picture of what happens to their information upon submission. Privacy policies are most effective when Web site visitors can locate, read and comprehend their terms. Armed with this knowledge, individuals are supposed to make accurate privacy assessments before submitting information online. Problematically, contemporary privacy policies fail to live up to their promise because they are posted inconspicuously, purposefully vague and filled with legalese. This inaccessibility leads Web surfers to ignore privacy practices completely while they continue to submit PII blindly.Privacy policies can be effective if companies clearly and conspicuously discuss how their privacy terms relate to fair information practices (FIPs). FIPs are widely agreed upon guidelines covering the most important areas of the data trade - PII collection, use, storage and dissemination. The Federal Trade Commission has designated the five core FIPs to be notice, choice, access, integrity and enforcement. This article argues that a standardized privacy nutrition label - similar to the labels required by the Nutrition Labeling and Education Act - posted conspicuously on all e-commerce homepages can increase policy effectiveness. These federally mandated labels require companies to discuss their privacy practices in relation to each Key FIP. Although companies need not adopt specific policy terms or run their practices through a governmental clearinghouse, they must honestly disclose their practices. This is true of even the most unpopular practices such as external PII dissemination. Over time, consumers will become aware of these standardized labels, begin to understand FIPs, differentiate between privacy-protective and privacy-invasive practices and make better decisions before submitting PII.

System and process for providing verification and validation of an individual's identity

Abstract: A system and process for providing the recording of provided health care transactions to an individual and the verification and validation of both the recipient and provider's identities. More particularly, to a system and process for providing verification and validation of an individual's identity for use in the prevention of identity theft and fraud in the medical industry and the recording of all medical related treatments and record requests by authorized providers, among other features. The verification and validation, referred to as “Positive Identification (PI)” for an individual who is seeking or in need of medical diagnosis and/or treatment may include a collection of novel processes by which a person's PI may be obtained and captured in an electronic database each time a person's medical information is accessed or treatment is provided, and in full compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996. This may substantially prevent fraud of medical services.

Economic Impact of Identity Theft in India: Lessons from Western Countries

Abstract: 2011 3rd International Conference on Information and Financial Engineering IPEDR vol12 (2011)