Abstract
Video surveillance of public spaces is a feature of modern society that has expanded quite quickly and in a pervasive way during the last decades becoming a fundamental need for both individual and collective security. But, as the sophistication of this type of systems increases, the concern about threat to individuals’ right of privacy raises as well. Indeed, the video surveillance systems could breach personal privacy because location is clearly one of the most sensitive people information. Hence, preserving location privacy while achieving utility from it, is a challenging problem demanding the investigation of researchers. This paper tackles this non-trivial issue by designing a novel privacy-preserving architecture able to anonymously monitoring people access at the entrance of critical areas in an indoor space. At the same time our approach is able to provide full accountability in case of an accident or a legal requirement. Interestingly, our protocol is robust to server-side attacks and is efficient enough to be applied indoors through a set of IoT (Internet of Things) smart camera devices.
Highlights
In the last years, there has been a tremendous increase of video surveillance systems in public locations, both indoors and outdoors, such as stores, museums, stations, schools, and airports, with the objective of reducing crimes and maintain public order [1]–[4]
These monitoring systems track users continuously and generate an enormous amount of potentially sensitive information, which represents an intrinsic threat for people’s privacy [5]–[7]. These technologies are evolving towards the so called Internet of Multimedia Things, a novel paradigm defined as a network of smart devices requiring higher computational capabilities than classical sensors belonging to the Internet of Things [8]–[10]
This paper describes a framework to carry out privacy preserving surveillance leveraging a Secret Sharing paradigm
Summary
There has been a tremendous increase of video surveillance systems in public locations, both indoors and outdoors, such as stores, museums, stations, schools, and airports, with the objective of reducing crimes and maintain public order [1]–[4]. Logs could be used for identification; in particular, face recognition software can automatically identify people from face snapshots leveraging known sources, such as databases of drivers’ license photos, and thereby track their movements inside the monitored environment Though this security solution is essential and extremely important in case of accidents for accountability, the access logs stored in the dedicated servers expose people to privacy threats as they could be 1 continuously tracked, even by honest-but-curious providers, in the absence of suspicious events. The approach presented in [37] aims at enabling distributed secure processing and images storage, retaining the possibility to reconstruct the original data in case of a legal requirement In this approach, a camera computes N shards from an image and sends them to a number of independent servers, each performing some basic operations on its own shard. Its main drawback is that it cannot perform a-posteriori accountability in case of an accident or legal requirement, because the original videos are not maintained in a storage
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