Accessibility navigation

Holistic privacy impact assessment framework for video privacy filtering technologies

Badii, A., Al-Obaidi, A., Einig, M. and Ducournau, A. (2013) Holistic privacy impact assessment framework for video privacy filtering technologies. Signal & Image Processing: An International Journal, 4 (6). pp. 13-32. ISSN 2229 - 3922

[img] Text - Accepted Version
· Restricted to Repository staff only
· The Copyright of this document has not been checked yet. This may affect its availability.


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.5121/SIPIJ.2013.4602


ABSTRACT In this paper, we present a novel Holistic Framework for Privacy Protection Level Performance Evaluation and Impact Assessment (H-PIA) to support the design and deployment of privacy-preserving filtering techniques as may be co-evolved for video surveillance through user-centred participative engagement and collectively negotiated solution seeking for privacy protection. The proposed framework is based on the UI-REF methodology for Privacy by Co-Design which includes subjective-interpretivist and socio-psycho-cognitively rooted human judgment and decision making analysis. This supports not only the co-design of privacy filters but also the integration of Key Holistic Performance Indicators (KPIs) comprising a number of objective and subjective sub-spaces of the relevant evaluation metrics. For the objective tests, we have proposed five crucial criteria to be evaluated to assess the optimality of the balance of privacy protection and security assurance as may be offered by a given privacy filtering solution. The evaluation is supported by a process of quantitative assessment of some of the KPIs through an automated objective measurement of the functional performance of the given filter. Additionally, a subjective user study has been conducted to correlate with, and cross-validate, the results obtained from the objective assessment of the KPIs. The simulation results have confirmed the sufficiency, necessity and efficacy of the UI-REF-based methodologically-guided framework to enable optimally balanced Privacy Filtering whilst retaining the highest possible video-data intelligence. Insights from this study will serve the process of co-design of privacy-preserving video filtering solutions and other video-analytics privacy risk mitigation technologies.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:91012
Publisher:AIRCC Publishing Corporation

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation