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.

High resolution surveillance systems are essential for security. However, these powerful tools have been misused by several CCTV operators. The governments and civil society are attempting to strike a balance between safety and privacy. Privacy filters can be used to help protect part of an image which included Personally Identifiable Information (PII). This paper presents a novel approach to improve the privacy protection in the CCTV displays. Our method uses context cues to determine the required privacy filtering level for each person in the image. We also present a systematic methodology to handle the context cues. We use a rules engine to generalise and facilitate the customisation of this system that by design should be specialized for operation in a given environment. In addition, we present a case study as a proof-of-concept whereby we have created an environment providing with high levels of privacy protection whilst allowing the required level of surveillance monitoring.

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The goal of this work was to develop an easy and reliable equation for calculating seepage discharge through homogeneous earthen dams that operate with toe drains. For this purpose, many data were generated using (SEEP/W) program depending on the geometrical variables that affect this seepage, such as dam height (h), upstream water depth (H), dam top width (b), upstream and downstream slopes, and base length of toe drain. The seepage discharge quantity for 728 cases was determined using three different values for each of the aforementioned geometric variables. A straightforward and precise equation was created for computing the discharge via a homogeneous earth dam with chimney drain system using the dimensional analysis method and SPSS software, with an (R2) coefficient of 0.986. Then the dimensional analysis was applied to this information. A simple and accurate equation was obtained to calculate the discharge through a homogenous earth dam with a toe drain system with a coefficient (R2) of 0.986. The results indicated that the seepage discharge increases with an increase in the upstream water depth, the toe drain’s base length, and the angle of the upstream and downstream slopes, while it decreases with an increase in the dam top width. Additionally, utilizing the produced data, the artificial neural network model (ANN) was implemented. The results of this model revealed varying percentages of importance for the independent geometrical variables on the seepage quantity, where the upstream water depth (H) has the most important and amounted to 66.9%, while the upstream slope was the least important, where the importance percentage for this factor was 0.7%. The (ANN) model and predicted empirical equation results show excellent agreement when compared.

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