Abstract:This is the accepted version of the paper.This version of the publication may differ from the final published version.
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AbstractAs the world becomes an interconnected network where objects and humans interact with each other, new challenges and threats appear in the ecosystem. In this interconnected world, smart objects have an important role in giving users the chance for life-logging in smart environments. However, smart devices have several limitations with regards to memory, resou… Show more
“…They pointed out that the use of smartphones for lifelogging can make data private via encryption [31]; however, their approach failed to consider the privacy of bystanders who may be in the range of the logging device. Petroulakis et al [32] considered security and privacy issues in lifelogging in the smart environment and proposed a lightweight framework, with the focus on interconnectivity of devices and sharing of preferences and habits. They studied the energy consumption using a communication model and an attacker model using an experimental test-platform for secure sharing of lifelogs under different scenarios.…”
Pervasive logging devices capture everything along with the public nearby without their consent, thus, possibly troubling people who prefer their privacy. This has issues for privacy and, furthermore, the widespread use of such logging devices may affect people's behavior, as they may feel uncomfortable that they are constantly being monitored. People may wish to have some control over the lifelogging devices of others and, in this article, we describe a framework to restrict anonymous logging, unless explicitly permitted. Our privacy framework allows the user of a logging device to define privacy policies controlling when, where and who to restrict from logging them. Moreover, it is possible to select which type of logging sensors to apply these restrictions. Evaluation results show that this approach is a practical method of configuring privacy settings and restricting pervasive devices from logging.
“…They pointed out that the use of smartphones for lifelogging can make data private via encryption [31]; however, their approach failed to consider the privacy of bystanders who may be in the range of the logging device. Petroulakis et al [32] considered security and privacy issues in lifelogging in the smart environment and proposed a lightweight framework, with the focus on interconnectivity of devices and sharing of preferences and habits. They studied the energy consumption using a communication model and an attacker model using an experimental test-platform for secure sharing of lifelogs under different scenarios.…”
Pervasive logging devices capture everything along with the public nearby without their consent, thus, possibly troubling people who prefer their privacy. This has issues for privacy and, furthermore, the widespread use of such logging devices may affect people's behavior, as they may feel uncomfortable that they are constantly being monitored. People may wish to have some control over the lifelogging devices of others and, in this article, we describe a framework to restrict anonymous logging, unless explicitly permitted. Our privacy framework allows the user of a logging device to define privacy policies controlling when, where and who to restrict from logging them. Moreover, it is possible to select which type of logging sensors to apply these restrictions. Evaluation results show that this approach is a practical method of configuring privacy settings and restricting pervasive devices from logging.
“…Lightweight ciphers are considered efficient and optimal solutions for the IoT because they provide security to small devices connected to networks. In addition, symmetric cryptography was shown to help with decreasing the devices' power consumption, which allows more small devices to be connected within the networks [16].…”
Section: Related Workmentioning
confidence: 99%
“…(The operations described above and depicted in Figures 2-4 make use of these data structures.) 1,3,4,5,6,9,12,13,16,19,20,21,22,25,28,37,38,41,42,45,46…”
Lightweight stream ciphers have attracted significant attention in the last two decades due to their security implementations in small devices with limited hardware. With low-power computation abilities, these devices consume less power, thus reducing costs. New directions in ultra-lightweight cryptosystem design include optimizing lightweight cryptosystems to work with a low number of gate equivalents (GEs); without affecting security, these designs consume less power via scaled-down versions of the Mutual Irregular Clocking KEYstream generator—version 2-(MICKEY 2.0) cipher. This study aims to obtain a scaled-down version of the MICKEY 2.0 cipher by modifying its internal state design via reducing shift registers and modifying the controlling bit positions to assure the ciphers’ pseudo-randomness. We measured these changes using the National Institutes of Standards and Testing (NIST) test suites, investigating the speed and power consumption of the proposed scaled-down version named MICKEY 2.0.85. The (85) refers to the new modified bit-lengths of each MICKEY 2.0 register. The results show that it is faster, requires less power, and needs fewer GEs. The proposed variant will enhance the security of applications, such asRadio-frequency identification (RFID) technology, sensor networks, and in Internet of things (IoT) in general. It also will enhance research on the optimization of existing lightweight cryptosystems.
“…To investigate the energy consumption of the di↵erent levels of privacy model, we extend our previously developed test-bed setup [10,11]. The experimental test-bed consists of three Digi XBee Pro 802.15.4 devices which correspond to CPS A, CPS B and CPS C. All devices are connected through their serial cable with Matlab.…”
Section: Test-bed Setupmentioning
confidence: 99%
“…In this paper, we extend our previous work in [9][10][11] by investigating attacks, challenges and methods to preserve privacy in user-centric thin clients such as CPSs. We analyze the most severe privacy challenges occurred from passive attacks, such as eavesdropping and tra c analysis, and from active attacks, such as impersonation and jamming.…”
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