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Moolika Gedara, K.; Nguyen, M.; Yan, W.Q.; Li, X.J. Video Blockchain. Encyclopedia. Available online: https://encyclopedia.pub/entry/51692 (accessed on 18 May 2024).
Moolika Gedara K, Nguyen M, Yan WQ, Li XJ. Video Blockchain. Encyclopedia. Available at: https://encyclopedia.pub/entry/51692. Accessed May 18, 2024.
Moolika Gedara, Kasun, Minh Nguyen, Wei Qi Yan, Xue Jun Li. "Video Blockchain" Encyclopedia, https://encyclopedia.pub/entry/51692 (accessed May 18, 2024).
Moolika Gedara, K., Nguyen, M., Yan, W.Q., & Li, X.J. (2023, November 16). Video Blockchain. In Encyclopedia. https://encyclopedia.pub/entry/51692
Moolika Gedara, Kasun, et al. "Video Blockchain." Encyclopedia. Web. 16 November, 2023.
Video Blockchain
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This entry is adapted from the peer-reviewed paper 10.3390/electronics12173621

Researchers explore video blockchain for establishing connectivity among vehicles in a smart city through utilizing blockchain technology.

video blockchain distributed network intelligent vehicular

1. Introduction

The rapid advancement of blockchain has significantly impacted multiple facets of society, necessitating the careful consideration of the complexities that play a vital role in community development and that enhance our daily lives. As researchers navigate this area, addressing both its positive and negative aspects becomes imperative. Therefore, in the context of contemporary technologies, ensuring security is of paramount importance. Particularly in smart cities, where sensitive information is abundant, the establishment of a highly secure repository is necessary.
The concept of a “smart city” represents an ambitious goal pursued by cities world-wide, driven by the intricate nature of urban environments and the potential offered by intelligent technologies. Within this context, it is crucial to thoroughly examine digital surveillance systems, as they hold significant importance today. Furthermore, prioritizing the safety and security of individuals by reducing crime rates and accidents stands as one of the primary objectives of smart cities [1][2].
In video surveillance [3], the primary objective is to capture video footage using vehicle cameras and securely store it onsite, leveraging blockchain to ensure data communication integrity and resistance against tampering and cyberattacks. Video surveillance serves multiple purposes, including crime solving, anomalous event detection, and privacy policy enforcement, making recorded video footage highly valuable for diverse applications. However, there exists a potential risk of malicious attackers, savvy hackers, or unauthorized third parties manipulating cameras and video repositories, rendering them ineffective in crime scenarios. These attacks may include false frame injection, data tampering, and privacy violations within a surveillance system.

2. Theoretical Frameworks for Blockchain

Researchers explore how computational methods can be applied to video surveillance, focusing on the video blockchain approach that predominantly protects user identification by using hashed public keys [4]. Previous work [3] stored video footage from surveillance cameras and ensured the integrity of video data. However, the work did not cover the storage of real videos in the blockchain, which plays a significant role in simplifying human lifestyles across various sectors, including energy, healthcare, and supply chains [5][6].
The key feature of blockchain is to enable a decentralized community and reach consensus on a transparent transaction history without relying on pre-established trust, thus mitigating double-spending attacks [7][8]. The use of hash functions, particularly in the context of proof-of-work, has proven effective in resisting distributed denial-of-service (DDoS) attacks, as demonstrated by bitcoin.
In the process of calculating blockchain data, cryptographic encryption is employed to establish connections between preceding and succeeding blocks [9]. Each block has a block header, which contains the information necessary to establish the link between adjacent blocks and a block body that typically stores transaction details and records [10].
In a blockchain, each block contains specific information, including a local number indicating the block position in the chain, the identity of the source or creator, the hash value of the previous block, a timestamp, and a Merkle root. The hash value of the preceding block is critical for maintaining the integrity and continuity of the blockchain. The timestamp serves multiple purposes, ensuring the reliability, authenticity, and traceability of the data while preventing tampering [11].

3. Surveillance System for Smart Cities Approaches

The concept of smart cities [1] emerged during the 2008 economic crisis when the world sought to leverage information and communication technologies (ICT) to become more intelligent and resource-efficient. Smart cities aim to achieve cost and energy savings, improved service delivery and quality of life, and reduced environmental footprints [12].
A smart city includes a surveillance system that captures videos and aims to enhance the integrity of the recorded data. In [13], using Blocksee, through the method of using the event of a car accident, detected through built-in accelerometers, relevant videos were cryptographically hashed and recorded through distributed storage on the blockchain. To ensure the integrity of the recorded videos, researchers leveraged the unique features of the distributed and tamper-proof characteristics offered by blockchain technology. In the blockchain, timestamping features are applied to verify and transfer unaltered data to a distributed repository. Similarly, Ref. [7] explored the use of blockchain-based systems to guarantee the storage of recorded data from closed-circuit television (CCTV) cameras in smart cities, preventing alteration or tampering. This mechanism assists law enforcement and clients in securing data recordings from digital surveillance systems by utilizing the metadata recorded in the blockchain. To ensure data security in a smart city, it is crucial to adhere to the principles of confidentiality, integrity, and availability (CIA) [14][15] in both video surveillance and video blockchain. Although video surveillance lacks built-in mechanisms to ensure secure data transfer, researchers can sort the videos in the correct order [16] and ensure their proper storage in a video blockchain without tampering. The video blockchain mechanism organizes the videos accurately in ascending or descending order from the video website and stores large data in the blockchain [17][18][19]. Additionally, blockchain addresses data integrity in systems such as medical record keeping and intelligent gas monitoring in smart cities [20][21][22].

4. Extracting Cryptographic Functions: Advances and Limitations

While considering smart cities worldwide, infrastructure security and data privacy emerge as two critical aspects. A distributed ledger, such as blockchain, offers enhanced security for connected electric vehicles [23]. Blockchain eliminates single points of failure and employs various cryptographic algorithms to ensure data integrity. Bitcoin, implemented with blockchain since 2009 [13][24], has extended its support to sophisticated sectors like smart cities. Blockchain has also eliminated the need for third-party involvement in transactions, providing a secure and transparent mechanism [25].
Hashing, a one-way function, guarantees transaction information without tampering. By converting plain text to irreversible hash data, the blockchain employs hashing algorithms such as SHA for data encryption. The blockchain encrypts transaction data in blocks using a hash algorithm and saves a unique string of 32-bit numbers mixed with an arbitrary three-column string. The integrity of the stored data is ensured through comparisons between hash values. To create a high-confidence smart city, researchers propose an approach that combines a (k, n) secret-sharing mechanism and software-defined networking (SDN) framework to secure data transportation among smart cities [26]. Data security is guaranteed by using the (k, n) secret-sharing scheme, while SDN-based transmission strategies leverage SDN’s advantages in network management and are scheduled to overcome challenges posed by unstable network states. Extensive experiments demonstrated that this approach significantly reduces attack success rates with reasonable overhead.
A cryptographic hash function plays a crucial role in a blockchain by mapping arbitrary data to a fixed-size string. The security requirements for hash functions include one-way characteristics and collision resistance [16]. To ensure a minimum of 80-bit security, the output length of hash functions should be at least 160 bits. In blockchains, the general hash function is SHA256, which belongs to the SHA (secure hash algorithms) family of cryptographic hash functions. Hash functions in blockchains are utilized for various purposes, such as proof-of-work (PoW), address generation, block generation (as part of the Merkle-tree paradigm), signature validation, pseudorandom number generation, and other essential components, like the Fiat–Shamir mechanism.
A Merkle tree is employed to organize and represent the primary transaction data in a bottom-up manner. Each leaf node corresponds to a transaction, and the hash value of two transactions is computed to obtain the hash value of the intermediate node. This process continues until a final hash value, known as the Merkle root, is derived. Each set of transaction data has a unique Merkle root associated with it.
The Schnorr signature scheme reduces the computation required for signature generation, especially during idle time. The main computational work for generating signatures is independent of the message, and it involves multiplying a 2n-bit integer with an n-bit integer. The scheme is based on a prime modulus, p, where p − 1 has a prime factor, q, of an appropriate size, satisfying the condition p − 1 = 1 (mod q) [16]. Typically, p is chosen to be approximately 21,024, and q is approximately 2160, resulting in a 1024-bit number for p and a 160-bit number, which matches the length of the SHA-1 hash value [24].
In the field of cryptography, the Schnorr signature scheme is well known for its simplicity and security based on the intractability of discrete logarithm problems [27]. This scheme minimizes the computational effort required to generate a signature, and it is widely utilized in public-key cryptography for digital signatures. Digital signatures provide message authentication among communicating parties, protecting against fraudulent message creation or denial. Digital signatures involve the use of public-key algorithms among the communicating parties. The sender’s private key is harnessed to encrypt either the entire message or a hash code of the message, forming the digital signature. Confidentiality can be further achieved by encrypting the entire message along with the signature using public or private key schemes. To resolve disputes, the signature function and the message must be accessible to a third party. However, the security of these approaches relies on the protection of the sender’s private key against forgery, loss, or theft. Digital certificates and certificate authorities, along with timestamps and key revocation mechanisms, are commonly employed to address these security threats.

References

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  2. Alketbi, A.; Nasir, Q.; Abu Talib, M. Novel blockchain reference model for government services: Dubai government case study. Int. J. Syst. Assur. Eng. Manag. 2020, 11, 1170–1191.
  3. Deepak, K.; Badiger, A.N.; Akshay, J.; Awomi, K.A.; Deepak, G.; Harish Kumar, N. Blockchain-based management of video surveillance systems: A survey. In Proceedings of the International Conference on Advanced Computing and Communication Systems 2020, Coimbatore, India, 6–7 March 2020; pp. 1256–1258.
  4. Khan, M.A.; Salah, K. IoT security: Review, blockchain solutions, and open challenges. Future Gener. Comput. Syst. 2018, 82, 395–411.
  5. Engelhardt, M.A. Hitching healthcare to the chain: An introduction to blockchain technology in the healthcare sector. Technol. Innov. Manag. Rev. 2017, 7, 22–34.
  6. Fill, H.; Haerer, F. Knowledge blockchains: Applying blockchain technologies to enterprise modeling. In Proceedings of the Hawaii International Conference on System Sciences 2018, Waikoloa Village, HI, USA, 3–6 January 2018; pp. 4045–4054.
  7. Fitwi, A.; Chen, Y. Secure and privacy-preserving stored surveillance video sharing atop permissioned blockchain. In Proceedings of the International Conference on Computer Communications and Networks 2021, Athens, Greece, 19–22 July 2021; pp. 1–8.
  8. Fu, J.; Qiao, S.; Huang, Y.; Si, X.; Li, B.; Yuan, C. A study on the optimization of blockchain hashing algorithm based on PRCA. Secur. Commun. Netw. 2020, 2020, 8876317.
  9. Kullig, N.; Lämmel, P.; Tcholtchev, N. Prototype implementation and evaluation of a blockchain component on IoT devices. Procedia Comput. Sci. 2020, 175, 379–386.
  10. Hasan, O.; Brunie, L.; Bertino, E. Privacy-Preserving Reputation Systems Based on Blockchain and Other Cryptographic Building Blocks: A Survey. Ph.D. Thesis, University of Lyon, Lyon, France, 2023.
  11. Hou, L.; Zheng, K.; Liu, Z.; Xu, X.; Wu, T. Design and prototype implementation of a blockchain-enabled lora system with edge computing. IEEE Internet Things J. 2021, 8, 2419–2430.
  12. Khrais, L.T. The combination of iot-sensors in appliances and block-chain technology in smart cities energy solutions. In Proceedings of the International Conference on Advanced Computing and Communication Systems 2020, Coimbatore, India, 6–7 March 2020; pp. 1373–1378.
  13. Gallo, P.; Pongnumkul, S.; Nguyen, U.Q. BlockSee: Blockchain for IOT video surveillance in smart cities. In Proceedings of the IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe, Palermo, Italy, 12–15 June 2018.
  14. Chen, Y.C.; Chou, Y.P.; Chou, Y.C. An image authentication scheme using Merkle tree mechanisms. Future Internet 2019, 11, 149.
  15. Gřivna, T.; Drápal, J. Attacks on the confidentiality, integrity and availability of data and computer systems in the criminal case law of the Czech Republic. Digit. Investig. 2019, 28, 1–13.
  16. Khan, P.W.; Byun, Y.C.; Park, N. A data verification system for CCTV surveillance cameras using blockchain technology in smart cities. Electronics 2020, 9, 484.
  17. Lee, D.; Park, N. Blockchain based privacy preserving multimedia intelligent video surveillance using secure Merkle tree. Multimed. Tools Appl. 2020, 80, 34517–34534.
  18. Li, T.; Chen, Y.; Wang, Y.; Wang, Y.; Zhao, M.; Zhu, H.; Tian, Y.; Yu, X.; Yang, Y. Rational protocols and attacks in blockchain system. Secur. Commun. Netw. 2020, 2020, 8839047.
  19. Majdoubi, D.E.L.; El Bakkali, H.; Sadki, S. Towards smart blockchain-based system for privacy and security in a smart city environment. In Proceedings of the International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (2020), Marrakesh, Morocco, 24–26 November 2020.
  20. Chen, J.; Ruan, Y.; Guo, L.; Lu, H. BCVehis: A blockchain-based service prototype of vehicle history tracking for used-car trades in China. IEEE Access 2020, 8, 214842–214851.
  21. Chen, X.; Xing, Z.; Karki, B.; Li, Y.; Chen, Z. Blockchain simulation: A web application for it education. In Proceedings of the IEEE Annual Computing and Communication Workshop and Conference 2021, Virtual, 27–30 January 2021; pp. 486–491.
  22. Chukwu, E.; Garg, L. A systematic review of blockchain in healthcare: Frameworks, prototypes, and implementations. IEEE Access 2020, 8, 21196–21214.
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Subjects: Computer Science, Theory & Methods
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Kasun Moolika Gedara
,
Minh Nguyen
,
Wei Qi Yan
,
Xue Jun Li
View Times: 110
Revisions: 2 times (View History)
Update Date: 17 Nov 2023
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