Security and Privacy Challenges in Smart City

Submission Deadline: 23 February 2024 (closed) View: 136

Guest Editors

Dr. Chien-Ming Chen, Nanjing University of Information Science and Technology, China.
Dr. Saru Kumari, Ch. Charan Singh University, India.
Dr. Shehzad Ashraf Chaudhry, Abu Dhabi University, UAE.

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Summary

A smart city, mainly an urban society, utilizes Information and Communication Technologies (ICT) and ubiquitous computing, promoting a comfortable place, improving life quality, economic development, operational services to its citizen, and optimizing city functions. The United Nations report shows the world's population is estimated at 8.1 billion in 2025 and 9.6 billion in 2050. However, the report also claims that during the same period, the population growth will be in developing countries only, and there will be almost no alteration in developed countries' populations. Hence, we need smart cities not only for developed countries but also for developing countries in the coming years, particularly for India and China, the two most populated countries in the world.

To achieve the concept of a smart city, one needs cutting-edge infrastructure that interlinks millions of physical objects for communication and information exchange with the help of recent wired or wireless technologies. These objects, enabled by the Internet of Things, sense the real world and exchange information within or outside the network for decision-making. Furthermore, A smart city includes innovative technology at various levels, such as infrastructure, homes, vehicles, traffic and intersection, entertainment, health care, energy, environment, etc. However, the extensive coverage of smart objects and periodic information exchange brought severe security challenges and threats. For example, during the information exchange, the information is exposed to the public channel, causing various security threats, including replay, denial of service, data integrity theft attacks, and impersonation. Moreover, unnecessary exposure to the privacy of individuals can disclose the location of the individual at the time of traveling, promoting crime and other security breaches. Hence, secure solutions are recommended to ensure the security and privacy of data and individuals.

This special issue covers contributions toward the challenges and secure solutions for smart cities. These solutions may be based on various edge-cutting technologies, including Vehicular Adhoc Networks (VANET), the Internet of Things (IoT), Blockchain, cloud computing, Body Area Networks (BAN), smart traffic, etc. In a broader term, this issue aims to promote the research community toward achieving the privacy and security of smart cities for an individual's easy and comfortable life.

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Keywords

• Privacy preservation Intelligent ICT framework for a smart city
• Secure Blockchain-based solutions for a smart city
• IoT-enabled Edge/ fog and cloud technology for a smart city
• Energy-efficient based secure solutions Secure for the smart city
• A big-data-based secure solution for the smart city
• Machine learning-based detection of various frauds in smart city
• Transportation management of the smart city, including smart charging, parking, tracking of the vehicle
• IoT-enabled secure healthcare architecture for a smart city
• Secure solutions for wearable devices for a smart city
• Various secure services, such as e-voting and e-commerce, etc., for a smart city
• Secure Wearable devices for health monitoring
• Cryptographic protocol for the security and privacy of citizens
• State-of-art algorithm for Detection of security threats and its solutions in smart city

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Published Papers

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• Open Access

  ARTICLE

  Efficient DP-FL: Efficient Differential Privacy Federated Learning Based on Early Stopping Mechanism

  Sanxiu Jiao, Lecai Cai, Jintao Meng, Yue Zhao, Kui Cheng

  Computer Systems Science and Engineering, Vol.48, No.1, pp. 247-265, 2024, DOI:10.32604/csse.2023.040194
  （This article belongs to the Special Issue: Security and Privacy Challenges in Smart City)
  Abstract Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (Efficient DP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while More >

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• Open Access

  ARTICLE

  Hybrid Dynamic Optimization for Multilevel Security System in Disseminating Confidential Information

  Shahina Anwarul, Sunil Kumar, Ashok Bhansali, Hammam Alshazly, Hany S. Hussein

  Computer Systems Science and Engineering, Vol.47, No.3, pp. 3145-3163, 2023, DOI:10.32604/csse.2023.041061
  （This article belongs to the Special Issue: Security and Privacy Challenges in Smart City)
  Abstract Security systems are the need of the hour to protect data from unauthorized access. The dissemination of confidential information over the public network requires a high level of security. The security approach such as steganography ensures confidentiality, authentication, integrity, and non-repudiation. Steganography helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and receiver. Therefore, we present an efficient hybrid security model that provides multifold security assurance. To this end, a rectified Advanced Encryption Standard (AES) algorithm is proposed More >

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