ePrivateeye

Streiffer, Christopher; Srivastava, Animesh; Orlikowski, Victor; Velasco, Yesenia; Martin, Vincentius; Raval, Nisarg; Machanavajjhala, Ashwin; Cox, Landon P.

doi:10.1145/3132211.3134457

Cited by 20 publications

(3 citation statements)

References 17 publications

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“…Moreover, there have been some specific attempts to leverage the advancement in machine learning and object-detection technologies to localize privacy-sensitive objects [7][8][9]38,39]. These works give some insight about how to build privacy-aware real-time video analytics in the surveillance systems, but they offer no viable solution for preserving privacy.…”

Section: Advancements In Object-detection Methodsmentioning

confidence: 99%

“…In other words, the capabilities of the deployed cameras could be potentially exploited or abused which worsens the risk of the privacy breaches of individuals. As the surveillance cameras become more powerfully equipped with better optical and digital zooms, the more likely they are to be abused to peek via windows to gather unauthorized private information on activities or behavioral patterns of individuals and to perform voyeurism unless preventive measures are enforced by design [5,7,8]. For instance, maneuverable cameras, like pan-tilt-zoom (PTZ) cameras, could be abused and directed to intrusively spy on other people in their apartments like the case where a security guard spied on the private apartment of the German Chancellor Angela Merkel using a museum's CCTV camera [2].…”

Section: Introductionmentioning

confidence: 99%

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Privacy-Preserving Surveillance as an Edge Service Based on Lightweight Video Protection Schemes Using Face De-Identification and Window Masking

et al. 2021

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With a myriad of edge cameras deployed in urban and suburban areas, many people are seriously concerned about the constant invasion of their privacy. There is a mounting pressure from the public to make the cameras privacy-conscious. This paper proposes a Privacy-preserving Surveillance as an Edge service (PriSE) method with a hybrid architecture comprising a lightweight foreground object scanner and a video protection scheme that operates on edge cameras and fog/cloud-based models to detect privacy attributes like windows, faces, and perpetrators. The Reversible Chaotic Masking (ReCAM) scheme is designed to ensure an end-to-end privacy while the simplified foreground-object detector helps reduce resource consumption by discarding frames containing only background-objects. A robust window-object detector was developed to prevent peeping via windows; whereas human faces are detected by using a multi-tasked cascaded convolutional neural network (MTCNN) to ensure de-identification. The extensive experimental studies and comparative analysis show that the PriSE scheme (i) can efficiently detect foreground objects, and scramble those frames that contain foreground objects at the edge cameras, and (ii) detect and denature window and face objects, and identify perpetrators at a fog/cloud server to prevent unauthorized viewing via windows, to ensure anonymity of individuals, and to deter criminal activities, respectively.

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Section: Advancements In Object-detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Surveillance as an Edge Service Based on Lightweight Video Protection Schemes Using Face De-Identification and Window Masking

et al. 2021

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“…Similar research is conducted by a number of researchers (e.g., [27,28]). To that end, those approaches comply with anonymization approaches required to bypass GDPR regulations.…”

Section: Approaches To Preserve Privacy In Video-based Surveillancementioning

confidence: 97%

"Healthy surveillance": Designing a concept for privacy-preserving mask recognition AI in the age of pandemics

Kühl¹,

Martin²,

Wolff³

et al. 2020

Preprint

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The obligation to wear masks in times of pandemics reduces the risk of spreading viruses. In case of the COVID-19 pandemic in 2020, many governments recommended or even obligated their citizens to wear masks as an effective countermeasure.In order to continuously monitor the compliance of this policy measure in public spaces like restaurants or tram stations by public authorities, one scalable and automatable option depicts the application of surveillance systems, i.e., CCTV. However, large-scale monitoring of mask recognition does not only require a well-performing Artificial Intelligence, but also ensure that no privacy issues are introduced, as surveillance is a deterrent for citizens and regulations like General Data Protection Regulation (GDPR) demand strict regulations of such personal data. In this work, we show how a privacy-preserving mask recognition artifact could look like, demonstrate different options for implementation and evaluate performances. Our conceptual deep-learning based Artificial Intelligence is able to achieve detection performances between 95% and 99% in a privacy-friendly setting. On that basis, we elaborate on the trade-off between the level of privacy preservation and Artificial Intelligence performance, i.e. the "price of privacy".

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