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PRIVACY4FORENSICS

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Cerrado

A Formal Rule Processing Engine for Privacy Respecting Forensic Investigation

The forensics investigation requirements are in direct conflict with the privacy rights of those whose actions are being investigated. At the same time, once the private data is exposed it is impossible to ‘undo’ its exposure effe... ver más

04/03/2018

USAL

231K€

Presupuesto del proyecto: 231K€

Líder del proyecto

THE UNIVERSITY OF SALFORD No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 2 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2018-03-04 No tenemos la información de la convocatoria

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2 Participantes

USAL

0.00€ | Lider

GUC

N.D. | Participante

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Líder del proyecto

THE UNIVERSITY OF SALFORD No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 231K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The forensics investigation requirements are in direct conflict with the privacy rights of those whose actions are being investigated. At the same time, once the private data is exposed it is impossible to ‘undo’ its exposure effects should the suspect is found innocent! Therefore, there is a growing concern in the development of forensic investigation frameworks that respect privacy of the involved entities whiles only need-to-know data are exposed to the forensic investigator. To the best of our knowledge, there is no model for privacy-respecting digital forensic investigation that is capable of considering different jurisdictions requirements and protects subjects' data privacy in accordance with search warrant permissions and collected data requirements. In this proposal, we offer development of a rule-processing engine that extracts privacy properties of collected data and investigation search warrant(s), detects conflicting or uncertain situations and labels collected data accordingly, and monitors and controls investigator access to collected data in accordance with the assigned labels. The proposed model is capable of parsing any privacy policy (to support different rules and regulations in different contexts) and labels any type of collected-data while it keeps the soundness of the investigation by not amending the source data. The main contributions of this research are development of a formal language for capturing privacy properties of forensic investigation search warrant and collected data policies, devising objective criteria for comparing and contrasting privacy properties to detect potential conflicts or uncertain situations and making objective rulings, development of a practical strategy for labeling the collected data in accordance with privacy rulings, and innovation of a privacy-respecting forensics investigation controlling and monitoring engine.

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