Acxiom

About: Acxiom is a company organization based out in Little Rock, Arkansas, United States. It is known for research contribution in the topics: Personally identifiable information & Grid. The organization has 102 authors who have published 80 publications receiving 2240 citations. The organization is also known as: Acxiom Marketing Services.

SCAN: a structural clustering algorithm for networks

Abstract: Network clustering (or graph partitioning) is an important task for the discovery of underlying structures in networks. Many algorithms find clusters by maximizing the number of intra-cluster edges. While such algorithms find useful and interesting structures, they tend to fail to identify and isolate two kinds of vertices that play special roles - vertices that bridge clusters (hubs) and vertices that are marginally connected to clusters (outliers). Identifying hubs is useful for applications such as viral marketing and epidemiology since hubs are responsible for spreading ideas or disease. In contrast, outliers have little or no influence, and may be isolated as noise in the data. In this paper, we proposed a novel algorithm called SCAN (Structural Clustering Algorithm for Networks), which detects clusters, hubs and outliers in networks. It clusters vertices based on a structural similarity measure. The algorithm is fast and efficient, visiting each vertex only once. An empirical evaluation of the method using both synthetic and real datasets demonstrates superior performance over other methods such as the modularity-based algorithms.

Method and system for the creation, enhancement and update of remote data using persistent keys

Abstract: A method and system for the creation, enhancement and update of data on a customer database using data from a central database and persistent keys that are linked to each data structure is disclosed. The persistent keys allow the matching of equivalent data structures between the remote and central databases so that enhancement data from the central database can be overlaid onto the customer database. The persistent keys also make the update of data on the customer database more efficient, since key matching allows the transfer of only the data that is needed to update the customer database. The central database may physically contain all of the enhancement data, or may use a central database manager to transparently link a group of physically remote databases using the persistent keys. Data may be transferred from the central database to the customer database either in batch mode or in a near real-time mode through a direct table join. The linking feature of the persistent keys further allows the linkage of the customer database and the central database to create a multidimensional database.

Data linking system and method using tokens

Abstract: A method for linking data using permanent tokens is disclosed. The tokens may be used to link data pertaining to a consumer (20 and 24), a business, an address (26, 28 and 29), an occupancy, or a household. The tokens are created in a central repository (24), which maintains an identification class for each entity. The identification class contains all available information concerning the entity. The tokens may be applied to a data storage system to allow real-time construction of a total customer view (26, 28 and 29). The tokens may also be used to link the data storage system to a repository (24), such that the total customer view contains all available information concerning the customer. The total customer view may be used to formulate a response to customer input, such as a purchase or access to an Internet web page maintained by the data owner. Data updates may be performed incrementally, and may be pushed from the repository (24) to de data owner (26 and 28) as new information is recieved.

Secure data exchange technique

Abstract: Techniques utilizing common encryption approaches for data from multiple parties enable those parties to discover information that is held in common by the parties without disclosing to any party information that is not held in common by the parties. Encrypted information for each party can be compared to determine which encrypted values match, and those encrypted values can be returned to any of the parties such that a party can determine which corresponding data the parties have in common without having access to any other data of any other parties.

Threat modeling using attack trees

Abstract: A common practice for studying the risk to a business is based on risk management principles. I.e., security resources are applied to vulnerabilities that pose the greatest risk to the business. Several processes for identifying and prioritizing risk are proposed in the literature. One of the most effective is threat modeling. Traditional trust modeling thought in academia involved mostly mathematical and theoretical concepts and using computer-security company marketing literature/jargon making it very hard to understand or analyze. This paper presents a practical, high-level guide to understand the concepts of threat modeling to students in an introductory level Security course or even a Managerial course. We use the concept of Attack Trees to this end. Attack Trees are conceptual diagrams of threats on systems and possible attacks to reach those threats. Here, we first provide a brief introduction to Attack Trees and then we consider Globus systems to illustrate how Attack Trees could be used to analyze their security. We specifically evaluate the security of MyProxy system, an important security subsystem part of Globus toolkit for Grid Computing.