Privacy for IoT: Involuntary privacy enablement for smart energy systems

Ukil, Arijit; Bandyopadhyay, Soma; Pal, Arpan

doi:10.1109/icc.2015.7248377

Cited by 26 publications

(13 citation statements)

References 21 publications

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“…Security testing (51) Reports on test beds (47) Energy consumption testing (6) Interoperability Testing (7) Quality of Service (QoS) (7) Network-focused testbeds (10) Virtualized and cloud testbeds (9) End-to-End testbeds (23) Cybersecurity testbeds (5)…”

Section: Secure Strategies Architectures and Protocols (165)mentioning

confidence: 99%

“…Not far from these important areas for privacy, there are plenty of research papers that define privacy for specific applications of IoT. For example, Ukil et al [51] defines the privacy issues within smart energy systems. Here, the study describes the uniqueness of privacy within smart energy systems from the smart meter (component of the smart energy management system) point of view.…”

Section: Security Analysis Approaches and Techniques (23)mentioning

confidence: 99%

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Aspects of Quality in Internet of Things (IoT) Solutions: A Systematic Mapping Study

et al. 2019

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Internet of Things (IoT) is an emerging technology that has the promising power to change our future. Due to the market pressure, IoT systems may be released without sufficient testing. However, it is no longer acceptable to release IoT systems to the market without assuring the quality. As in the case of new technologies, the quality assurance process is a challenging task. This paper shows the results of the first comprehensive and systematic mapping study to structure and categories the research evidence in the literature starting in 2009 when the early publication of IoT papers for IoT quality assurance appeared. The conducted research is based on the most recent guidelines on how to perform systematic mapping studies. A set of research questions is defined carefully regarding the quality aspects of the IoT. Based on these questions, a large number of evidence and research papers is considered in the study (478 papers). We have extracted and analyzed different levels of information from those considered papers. Also, we have classified the topics addressed in those papers into categories based on the quality aspects. The study results carry out different areas that require more work and investigation in the context of IoT quality assurance. The results of the study can help in a further understanding of the research gaps. Moreover, the results show a roadmap for future research directions.

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Section: Secure Strategies Architectures and Protocols (165)mentioning

confidence: 99%

Section: Security Analysis Approaches and Techniques (23)mentioning

confidence: 99%

Aspects of Quality in Internet of Things (IoT) Solutions: A Systematic Mapping Study

et al. 2019

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“…A secondary and more feasible approach is the use of an outlier/anomaly detection algorithm at the EMU. Anomaly detection algorithms such as Z-score analysis [37], the modified Z-Score [38] or Kurtosis computation [39] enable the EMU with the capability of identifying the installation of a new appliance in the household which was not registered with the EMU earlier. With the help of these algorithms, the EMU keeps a look-out for any anomaly in the power utilization pattern.…”

Section: Step 1: Data Acquisition and Pre-processingmentioning

confidence: 99%

Increasing user controllability on device specific privacy in the Internet of Things

Asif

Rajarajan

Lestas

2018

Computer Communications

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This is the accepted version of the paper.This version of the publication may differ from the final published version.Permanent repository link: http://openaccess.city.ac.uk/19508/ Link to published version: http://dx. AbstractWith recent advancements in information technology more and more devices are integrated in the Internet of Things. These devices gather significant amount of private information pertinent to a user and while, in some cases it helps in improving the life style of an individual, in others it raises major privacy concerns. This trade-off between utility and privacy is highly dependent upon the devices in consideration and as the utility of the generated data increases, the privacy of an individual decreases. In this paper, we formulate a utility-privacy trade-off that enables a user to make appliance specific decisions as to how much data can be shared. This is achieved by parametrizing the degree of privacy allowed for each device and enabling the user to configure the parameter of each device. We use the smart metering application as the test case scenario for the proposed approach. We evaluate its performance using simulations conducted on the ECO data set. Our results indicate that, the proposed approach is successful in identifying appliances with an accuracy of 81.8% and a precision of 70.1%. In addition, it is demonstrated that device specific changes of the configuration parameters allow the degree of privacy achieved for the particular device and the utility to be well controlled, thus demonstrating the effectiveness of the proposed approach. Moreover, it is shown that, as expected, devices with higher power consumption contribute more to the overall privacy and utility achieved. A comparative study is also conducted and the proposed approach is shown to outperform the existing ElecPrivacy approach by producing a trace that is harder to identify, as reported after testing the Weiss' and Baranski's algorithm, both of which are well known Non-Intrusive Load Monitoring algorithms. Finally, it is demonstrated that the addition of noise, which is an integral part of the propose approach, can greatly improve performance.

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“…Highlighted IoT Privacy Threats cryptographic algorithms, control access management tools, data minimization techniques, and privacy or context awareness protocols (Table I). [1], [67], [44], [36], [18], [53], [22], [5], [64], [30], [7], [13], [77], [59], [73] Data minimization 3 [15], [7], [59] Access control 6 [1], [31], [30], [13], [39], [59] Privacy awareness or context awareness 12 [1], [31], [55], [5], [81], [7], [71], [70], [77], [69], [59], [8] Differential Privacy 0 Other (introspection, trust assessment and evaluation) 3 [34], [6], [40] Not Evaluated Cryptographic techniques and information manipulation 16 [24], [25], [79], [9], [20], [51], [41], [54], [43], [26], [...…”

Section: Threats Solutions Principles Perceptionsmentioning

confidence: 99%

“…Some solutions deploy access control methods, or privacy-awareness applications. For example, in [71], the study proposed the Dynamic Privacy Analyzer (DPA), a solution to make the smart-meter data owner aware of the privacy risks of sharing smart meter data with third parties. On the other hand, almost half of the proposed solutions proposed taking the human out of the loop.…”

Section: Gapsmentioning

confidence: 99%