Secure Data Sharing in Smart Homes: An Efficient Approach Based on Local Differential Privacy and Randomized Responses

Smart homes are smart spaces that contain devices that are connected to each other, collecting information and facilitating users’ comfortable living, safety, and energy management features. To improve the quality of individuals’ life, smart device companies and service providers are collecting data...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2023, Vol.14 (8)
Main Authors: Elsayed, Amr T. A., Alsharkawy, Almohammady S., Farag, Mohamed S., Abo-Youssef, S. E.
Format: Article
Language:English
Subjects:
Algorithms
Classification
Data collection
Data transmission
Energy management
Machine learning
Perturbation
Power consumption
Power management
Privacy
Safety management
Smart buildings
Smart houses
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Summary:Smart homes are smart spaces that contain devices that are connected to each other, collecting information and facilitating users’ comfortable living, safety, and energy management features. To improve the quality of individuals’ life, smart device companies and service providers are collecting data about user activities, user needs, power consumption, etc.; these data need to be shared with companies with privacy-preserving practices. In this paper, an effective approach of securing data transmission to the service provider is based on local differential privacy (LDP), which enables residents of smart homes to provide statistics on their power usage as disturbances bloom filters. Randomized Aggregatable Privacy-Preserving Ordinal (RAPPOR) is a privacy technique that allows sharing of data and statistics while pre-serving the privacy of individual users. The proposed approach applies two randomized responses: permanent random response (PRR) and instantaneous random response (IRR), then applies machine learning algorithms for decoding the perturbation bloom filters on the service provider side. The simulation results show that the proposed approach achieves good performance in terms of privacy-preserving, accuracy, recall, and f-measure metrics. The results indicate that, the proposed LDP for smart homes achieved good utility privacy when the value of LDP ϵ = 0.95. The classification accuracy is between 95.4% and 98% for the utilized classification techniques.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2023.01408121