Abstract: A data attribution system uses a unique identifier (UID) that uniquely identifies a particular individual. A search is conducted of different data sources and, different types of profile information associated with the UID is extracted from the data sources. The different types of profile information associated with the same UID is aggregated together and displayed in a same screen presentation on a user interface.

Abstract: An anonymous information system (AIS) maintains privacy for internet users by separating personally identifiable information (PII) and user browser history. The AIS may receive a hashed email address from a publisher website. The AIS system may double hash the email address and discard the hashed email address received from the publisher. The double hashed email address is decoupled from user PII and may be used for tracking the number of unique email addresses provided by the publisher website. The AIS system also may associate the hashed email address with a consumer file. The AIS may hash an entity identifier associated with the consumer file to create a non-reversible anonymous identifier (anonymous ID). The anonymous ID may be associated with segment information in the consumer file that does not contain PII about the user. The AIS may send customized information to users based on the segment information.

Abstract: A data attribution system uses a unique identifier (UID) that uniquely identifies a particular individual. A search is conducted of different data sources and, different types of profile information associated with the UID is extracted from the data sources. The different types of profile information associated with the same UID is aggregated together and displayed in a same screen presentation on a user interface.

Abstract: A data attribution system uses a unique identifier (UID) that uniquely identifies a particular individual. A search is conducted of different data sources and, different types of profile information associated with the UID is extracted from the data sources. The different types of profile information associated with the same UID is aggregated together and displayed in a same screen presentation on a user interface.

Abstract: An anonymous information system (AIS) maintains privacy for internet users by separating personally identifiable information (PII) and user browser history. The AIS may receive a hashed email address from a publisher website. The AIS system may double hash the email address and discard the hashed email address received from the publisher. The double hashed email address is decoupled from user PII and may be used for tracking the number of unique email addresses provided by the publisher website. The AIS system also may associate the hashed email address with a consumer file. The AIS may hash an entity identifier associated with the consumer file to create a non-reversible anonymous identifier (anonymous ID). The anonymous ID may be associated with segment information in the consumer file that does not contain PII about the user. The AIS may send customized information to users based on the segment information.

Abstract: A user correlation system uses aggregated data and matching/comparison in order to assign an association score that determines likelihood that unique identifiers are associated with the same user.

Abstract: A user correlation system uses aggregated data and matching/comparison in order to assign an association score that determines likelihood that unique identifiers are associated with the same user.

Abstract: A reputability analysis system receives a domain assessment request associated with a domain and accesses a database to find a match for the domain. A reputability score is derived according to a hierarchical analysis of a matching domain in the database. Traceability, accountability, and association information associated with the domain assessment request may also be used to adjust the reputability score.

Abstract: A data attribution system uses a unique identifier (UID) that uniquely identifies a particular individual. A search is conducted of different data sources and, different types of profile information associated with the UID is extracted from the data sources. The different types of profile information associated with the same UID is aggregated together and displayed in a same screen presentation on a user interface.

Abstract: A reputability analysis system receives a domain assessment request associated with a domain and accesses a database to find a match for the domain. A reputability score is derived according to a hierarchical analysis of a matching domain in the database. Traceability, accountability, and association information associated with the domain assessment request may also be used to adjust the reputability score.

Abstract: The present invention is directed to collaborative document review. In one embodiment, the method includes creating a review version of an original document and storing the review document in a location that is simultaneously accessible by more than one reviewer, where each reviewer might be in a different location. Each reviewer is able to review at least one section of the review document and annotate, or comment on, the selected section of the review document. Once the annotation is created, the annotation can be posted and be immediately visible to each reviewer. Each reviewer can also provide further comment on any annotation. Annotations made in an earlier version of the review document can be retained and carried over into subsequent versions of the review document.

Abstract: A method and system for searching for text in a document. In one embodiment, the method includes comparing a signature of text to be located with a signature of each section of text in the document. A distance from an expected location of the text to be matched is computed and compared to a location of each section of text in the document. An exact match of the signature of text to be located that is nearest to the expected location of the text to be located is sought. If an exact match of the signature is not found at the expected location, a close match to the signature, that is nearest to the expected location, is sought. If the exact match is found, the location of the exact match is identified as the location of the text being searched for. If the exact match is not found, and a close match is identified, the close match is identified as the location of the text being searched for.