Privacy is an important social and political issue of increasing concern in our networked society, characterised by a growing range of enabling and supporting technologies and services. These include communications, multimedia, biometrics, audio-video surveillance, big data, data mining, cloud computing, internet, on line mapping services, and social networks. Each of these technologies and services
can potentially be used as a basis for privacy intrusion. Efforts to address this rapidly growing problem over recent years have led to the emergence of de-identification as a central approach for the preservation of privacy in different scenarios of interest. Whilst the research undertaken to date in academia and industry has resulted in extensive progress in this field, there are still many open problems in deidentification (e.g. multimodal and context de-identification, naturalness of de-identified videos, real-time detection of several ROIs (face, body silhouette, accessories) in dynamic scenes, voice de-identification in situations where there are multiple individuals speaking simultaneously) that need addressing in order to achieve the required effectiveness in privacy preservation. The aim of this Special Issue is to establish the latest advances and current challenges in different signal processing aspects of de-identification, ranging from novel reversible and non-reversible algorithms/methods for unimodal/multimodal de-identification, to metrics and protocols for the evaluation of privacy protection, and for the assessment of the utility, naturalness and intelligibility of de-identified data. This Special Issue is associated with COST Action IC1206 “De-identification for Privacy Protection in Multimedia Content”. Therefore, researchers in this COST Action are particularly invited to submit papers. However, contribution to this Special Issue is open to all researchers working in the field and they are strongly encouraged to make a submission.

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