TY - GEN
T1 - Privacy-Enhanced Living
T2 - 2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023
AU - Waheed, Nazar
AU - Khan, Fazlullah
AU - Mastorakis, Spyridon
AU - Jan, Mian Ahmad
AU - Alalmaie, Abeer Z.
AU - Nanda, Priyadarsi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Hidden Markov Chain
KW - Internet of Things
KW - Local Differential Privacy
KW - Security
KW - Smart Homes
UR - http://www.scopus.com/inward/record.url?scp=85167873177&partnerID=8YFLogxK
U2 - 10.1109/COINS57856.2023.10189261
DO - 10.1109/COINS57856.2023.10189261
M3 - Conference contribution
AN - SCOPUS:85167873177
T3 - 2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023
BT - 2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 July 2023 through 25 July 2023
ER -
