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Smart city article details
| Title | Privacy-Enhanced Living: A Local Differential Privacy Approach To Secure Smart Home Data |
|---|---|
| ID_Doc | 43152 |
| Authors | Waheed N.; Khan F.; Mastorakis S.; Jan M.A.; Alalmaie A.Z.; Nanda P. |
| Year | 2023 |
| Published | 2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023 |
| DOI | http://dx.doi.org/10.1109/COINS57856.2023.10189261 |
| Abstract | The rapid expansion of Internet of Things (IoT) devices in smart homes has significantly improved the quality of life, offering enhanced convenience, automation, and energy efficiency. However, this proliferation of connected devices raises critical concerns regarding security and privacy of the user data. In this paper, we propose a differential privacy-based system to ensure comprehensive security for data generated by smart homes. We employ the randomized response technique for the data and utilize Local Differential Privacy (LDP) to achieve data privacy. The data is then transmitted to an aggregator, where an obfuscation method is applied to ensure individual anonymity. Furthermore, we implement the Hidden Markov Model (HMM) technique at the aggregator level and apply differential privacy to the private data received from smart homes. Consequently, our approach achieves a dual layer of privacy protection, addressing the security concerns associated with IoT devices in smart cities. © 2023 IEEE. |
| Author Keywords | Hidden Markov Chain; Internet of Things; Local Differential Privacy; Security; Smart Homes |
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