A Scalable and Privacy-Aware IoT Service for Live Video Analytics

Wang, Junjue; Amos, Brandon; Das, Anupam; Pillai, Padmanabhan; Sadeh, Norman; Satyanarayanan, Mahadev

doi:10.1145/3083187.3083192

Cited by 98 publications

(44 citation statements)

References 24 publications

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“…Video analysis integrated in IoT networks is strongly supported by neural networks, e.g., deep learning-based visual food recognition allows for the construction of a system employing an edge computing-based service for accurate dietary assessment [98]. RTFace, a mechanism for denaturing video streams, has been based on a Deep Neural Network for face detection [99]. It selectively blurs faces and enables privacy management for live video analytics.…”

Section: Containment Of Conjunctive Queries On Annotated Relationsmentioning

confidence: 99%

Review of the Complexity of Managing Big Data of the Internet of Things

et al. 2019

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There is a growing awareness that the complexity of managing Big Data is one of the main challenges in the developing field of the Internet of Things (IoT). Complexity arises from several aspects of the Big Data life cycle, such as gathering data, storing them onto cloud servers, cleaning and integrating the data, a process involving the last advances in ontologies, such as Extensible Markup Language (XML) and Resource Description Framework (RDF), and the application of machine learning methods to carry out classifications, predictions, and visualizations. In this review, the state of the art of all the aforementioned aspects of Big Data in the context of the Internet of Things is exposed. The most novel technologies in machine learning, deep learning, and data mining on Big Data are discussed as well. Finally, we also point the reader to the state-of-the-art literature for further in-depth studies, and we present the major trends for the future.

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Section: Containment Of Conjunctive Queries On Annotated Relationsmentioning

confidence: 99%

Review of the Complexity of Managing Big Data of the Internet of Things

et al. 2019

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“…Furthermore, the BVC is well-positioned to address privacy concerns that arise from recording people in such video feeds. The BVC has enough compute to denature images as done in [19].…”

Section: Big Vehicle Cloudletmentioning

confidence: 99%

Towards a Distraction-free Waze

Christensen

Mertz

Pillai

et al. 2019

Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications

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Real-time traffic monitoring has had widespread success via crowdsourced GPS data. While drivers benefit from this low-level, lowlatency road information, any high-level traffic data such as road closures and accidents currently have very high latency as such systems rely solely on human reporting. Increasing the detail and decreasing the latency of this information can have significant value. In this paper we explore this idea by using a camera along with an in-vehicle computer to run computer vision algorithms that continuously observe the road conditions in high-detail. Abnormalities are automatically reported via 4G LTE to a local server on the edge, which collects and stores the data, and relays updates to other vehicles inside its zone. In this paper we develop and test such a system, which we call LiveMap. We demonstrate its accuracy on detecting hazards and characterize the system latency achieved.

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“…Wang et al [83] introduce a scalable privacy-aware IoT architecture that enables live video analytics across many cameras by combining OpenFace [95], a high accuracy opensource face recognizer, with face tracking to maintain high accuracy and achieve full frame rate speeds. Authors also use privacy mediators to enforce user-defined privacy policies (e.g., face denaturing), yet the system maintains the original videos for possible future needs (e.g., finding an evidence from a crime scene).…”

Section: Data Anonymizationmentioning

confidence: 99%

Towards Privacy Preserving IoT Environments: A Survey

Seliem

Elgazzar

Khalil

2018

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is a network of Internet-enabled devices that can sense, communicate, and react to changes in their environment. Billions of these computing devices are connected to the Internet to exchange data between themselves and/or their infrastructure. IoT promises to enable a plethora of smart services in almost every aspect of our daily interactions and improve the overall quality of life. However, with the increasing wide adoption of IoT, come significant privacy concerns to lose control of how our data is collected and shared with others. As such, privacy is a core requirement in any IoT ecosystem and is a major concern that inhibits its widespread user adoption. The ultimate source of user discomfort is the lack of control over personal raw data that is directly streamed from sensors to the outside world. In this survey, we review existing research and proposed solutions to rising privacy concerns from a multipoint of view to identify the risks and mitigations. First, we provide an evaluation of privacy issues and concerns in IoT systems due to resource constraints. Second, we describe the proposed IoT solutions that embrace a variety of privacy concerns such as identification, tracking, monitoring, and profiling. Lastly, we discuss the mechanisms and architectures for protecting IoT data in case of mobility at the device layer, infrastructure/platform layer, and application layer.

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A Scalable and Privacy-Aware IoT Service for Live Video Analytics

Cited by 98 publications

References 24 publications

Review of the Complexity of Managing Big Data of the Internet of Things

Review of the Complexity of Managing Big Data of the Internet of Things

Towards a Distraction-free Waze

Towards Privacy Preserving IoT Environments: A Survey

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