We study the problem of disinformation. We assume that an “agent” has some sensitive information that the “adversary” is trying to obtain. For example, a camera company (the agent) may secretly be developing its new camera model, and a user (the adversary) may want to know in advance the detailed specs of the model. The agent’s goal is to disseminate false information to “dilute” what is known ...

Entity resolution (ER), also known as duplicate detection or record matching, is the problem of identifying the tuples that represent the same real world entity. In this paper, we address the problem of ER for uncertain data, which we call ERUD. We propose two different approaches for the ERUD problem based on two classes of similarity functions, i.e. context-free and context-sensitive. We prop...

Entity resolution (ER) is the problem of identifying which records in a database refer to the same entity. Although ER is a well-known problem, the rapid increase of data has made ER a challenging problem in many application areas ranging from resolving shopping items to counter-terrorism. The SERF project at Stanford focuses on providing scalable and accurate ER techniques that can be used acr...

Entity resolution is an important graph mining problem. Entity resolution is particularly interesting and challenging when there is rich relational structure. In this paper, we study the problem of performing entity resolution in familial networks. In our setting, we are given partial views of a familial network as described from the point of view of different people in the network and our goal...

Entity resolution (ER) identifies database records that refer to the same real world entity. In practice, ER is not a one-time process, but is constantly improved as the data, schema and application are better understood. We address the problem of keeping the ER result up-to-date when the ER logic “evolves” frequently. A naı̈ve approach that re-runs ER from scratch may not be tolerable for resol...

Entity resolution is the process of discovering groups of tuples that correspond to the same real world entity. In order to avoid the prohibitively expensive comparison of all pairs of tuples, blocking algorithms separate the tuples into blocks which are highly likely to contain matching pairs. Tuning is a major challenge in the blocking process. In particular, contemporary blocking algorithms ...

A major emerging problem among consumer finance institutions is that customers that are not well recognized might be riskier than customers that are fully recognized. Fortunately, financial institutions count with external vendors databases that indicate the level of recognition of their customers. However, this information is normally presented as features with partial scores that must be aggr...

In this paper, we study a hybrid human-machine approach for solving the problem of Entity Resolution (ER). The goal of ER is to identify all records in a database that refer to the same underlying entity, and are therefore duplicates of each other. Our input is a graph over all the records in a database, where each edge has a probability denoting our prior belief (based on Machine Learning mode...

Complex networks can be used to describe the Internet, social network, or more broadly describe a binary relation of a set of objects. Structure information of complex network helps the identification of the entity corresponding to nodes in the network. There is much research in this area, and the authors introduce these studies and their results in this chapter. The authors mainly present two ...