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 normative ethno-methodological framework for Privacy-by-Co-Design which is based on collective-interpretivist and socio-psycho-cognitively rooted Human Judgment and Decision Making (JDM) theory including Pleasure-Pain-Recall (PPR)-theoretic opinion elicitation and analysis. This supports not only the socio-ethically reflective conflicts resolution, prioritisation and traceability of privacy-preserving requirements evolving through user-centred co-design but also the integration of Key Holistic Performance Indicators (KPIs) comprising a number of objective and subjective evaluation metrics for the design and operational deployment of surveillance data/-video-analytics from a system-of-system-scale context-aware accountability engineering perspective. 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 negotiated with end-users through co-design of a privacy filtering solution. This 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 qualitative 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 for Privacy Protection evaluation to enable optimally balanced Privacy Filtering of the video frame whilst retaining the minimum of the information as negotiated per agreed process logic. Insights from this study have served the co-design and deployment optimisation of privacy-preserving video filtering solutions. This UI-REF-based framework has been successfully applied to the evaluation of MediaEval 2012-2013 Privacy Filtering and as such has served to motivates further innovation in co-design and multi-level, multi-modal impact assessment of multimedia privacy-security-balancing risk mitigation technologies.

With the growing need for privacy-aware and privacy-respecting CCTV systems, it becomes crucial to develop workflows and architectures that can support and enhance privacy protection. Recent advances in image processing enable the automation of many surveillance tasks, increasing the risks of privacy infringements.Fortunately, image processing and pattern recognition techniques can also be used for automatically evaluating the context in which video surveillance takes place, and can therefore be employed for applying context-specific privacy rules. This paper describes how Bag-of-Visual-Words algorithms as well as human tracking and gait analysis cane used for recognizing specific sub-contexts that necessitate the application of particular privacy protection rules in usage contexts such as ambient assisted living, public or workspace surveillanceWe explain how the data of a multi-modal surveillance system should be handled in order to avoid unnecessary processing of sensitive information through Image Quality Descriptors that will support visual classifications by computing reliability measures relating to the image quality such as noise or problems with respect to ambient conditions.

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