Inference Management: Trust and Obfuscation in Coalition Settings

Abstract

In modern coalition operations, decision makers must be capable of obtaining and fusing information from diverse sources. The reliability of these sources can vary, and, to protect their interests, the information these sources provide could be altered, e.g., obfuscated, to limit the inferences that can be made with it. The trustworthiness of fused information depends on both the reliability of these sources and their inference management policies. Information consumers must determine how to evaluate trust in the presence of inference management techniques, such as obfuscation, while information providers must determine the appropriate level of obfuscation to ensure both that they remain trusted, and do not reveal any private information. In this paper, we present and formalize trust in the context of inference management and discuss the relationships between the two. We illustration the pertinent concepts via a multi-party coalition scenario, and present numerical examples, using subjective logic computational techniques, of how trust and obfuscation can influence belief levels in information gathered.

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Document Details

Document Type
Technical Report
Publication Date
Sep 20, 2012
Accession Number
ADA565914

Entities

People

  • Chatschik Bisdikian
  • Christopher Burnett
  • Lance Kaplan
  • Mani B. Srivastava
  • Murat Şensoy
  • Nir Oren
  • Timothy J. Norman

Organizations

  • IBM Thomas J. Watson Research Center

Tags

Communities of Interest

  • Biomedical
  • Engineered Resilient Systems
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Agreements
  • Consumers
  • Contracts
  • Detectors
  • Equations
  • Governments
  • Information Exchange
  • Information Operations
  • Information Processing
  • Information Systems
  • Joint Military Activities
  • Low Resolution
  • Military Operations
  • Military Research
  • Multiagent Systems
  • Reasoning
  • Reliability

Fields of Study

  • Computer science

Readers

  • Cybersecurity.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML