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System and Method for Trust Network using Provenance of Digital Entities

IP.com Disclosure Number: IPCOM000237876D
Publication Date: 2014-Jul-17
Document File: 2 page(s) / 43K

Publishing Venue

The IP.com Prior Art Database


Disclosed is a method to incorporate a document’s provenance into a Bayesian belief network using a provenance chart to show the relationships between the document’s sources. Knowing the history and associated trust levels of the components of a document influences the developer’s and readers’ overall confidence in the accuracy and quality of the document.

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System and Method for Trust Network using Provenance of Digital Entities

The provenance of digital objects represents the associated origins. Provenance (PROV) records contain descriptions of the entities and activities involved in producing and delivering (and otherwise influencing) a given object. PROV describes how these objects were created or delivered. Knowing the provenance of an object helps developers determine how to use it.

Provenance can be used for many purposes, such as:
 Understanding how data was collected so that it can be meaningfully used

 Determining ownership and rights over an object

 Making judgments about information to determine whether to trust it

 Verifying that the process and steps used to obtain a result comply with given requirements

 Reproducing how something was generated

This disclosure proposes the incorporation of provenance into a Bayesian belief network. Notions of trust and confidence influence a belief network. This example demonstrates a hypothetical situation and builds a probabilistic model (i.e. belief network) based on all possible outcomes (for this simple case) and a few known facts. The model then incorporates new evidence, which changes user beliefs.

In this hypothetical case of a document, User A is writing a document for which a chart was borrowed from User B, who re-used the source data from User C. User C compiled the source data from a variety of sources, including a number of websites and other users.

Figure: The complete provenance chart representing this set of relationships

The level of trust that a reader has in User A's document is based on the trust that the


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reader has (and, in turn, other readers might have) in the agents, entities, and processes involved in the creation of that document. By exposing a mechanism whereby a reader can leave feedback against entities, this feedback can be incorporated into the model. A reader might choose to rate the entire article. For this example, assume a rating scale of 1-5 stars, where 1=low trust and 5=high trust. This feedback m...