Skip to Main content Skip to Navigation
Theses

OPP_IoT An ontology-based privacy preservation approach for the Internet of Things

Abstract : The spread of pervasive computing through the Internet of Things (IoT) represents a challenge for privacy preservation.Privacy threats are directly related to the capacity of the IoT sensing to track individuals in almost every situation of their lives.Allied to that, data mining techniques have evolved and has been used to extract a myriad of personal information from sensor data stream.This trust model relies on the trustworthiness of the data consumer who should infer only intended information.However, this model exposes personal information to privacy adversary.In order to provide a privacy preservation for the IoT, we propose a privacy-aware virtual sensor model that enforces privacy policy in the IoT sensing as a service.This mechanism intermediates physical sensors and data consumers.As a consequence, we are able to optimize the use of privacy preserving techniques by applying them selectively according to virtual sensor inference intentions, while preventing malicious virtual sensors to execute or get direct access to raw sensor data.In addition, we propose an ontology to classify personal information based on the Behavior Computing, facilitating privacy policy definition and information classification based on the behavioral contexts.
Document type :
Theses
Complete list of metadatas

https://tel.archives-ouvertes.fr/tel-01681206
Contributor : Abes Star :  Contact
Submitted on : Friday, January 12, 2018 - 2:09:09 PM
Last modification on : Tuesday, October 20, 2020 - 11:23:12 AM
Long-term archiving on: : Wednesday, May 23, 2018 - 8:07:41 PM

File

MOREIRA_DA_COSTA_2017_archivag...
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-01681206, version 2

Collections

STAR | LIG | UGA | CNRS

Citation

Thiago Moreira da Costa. OPP_IoT An ontology-based privacy preservation approach for the Internet of Things. Web. Université Grenoble Alpes, 2017. English. ⟨NNT : 2017GREAM003⟩. ⟨tel-01681206v2⟩

Share

Metrics

Record views

518

Files downloads

1398