245

Blockchain in Digital Forensics

can indicate the deletion. All these challenges make the acquisition of data from

blockchain-based distributed cloud storage more complex. A lot of research has been

required for developing the methods/applications required for recovering data from

blockchain-based distributed cloud storage for digital forensics.

14.7  CONCLUSION

This chapter has provided a brief introduction to blockchain technology along with

its unique properties that make it different from traditional databases. After that, its

different application areas, like healthcare, IoT, government, advertising and legal

perspectives, have been discussed. Then, the challenges faced by blockchain tech­

nology, its architecture and the protocols have been discussed. After that, the process

of managing CoC for digital evidence (preserving and recording digital documenta­

tion historical history) in Ethereum and Hyperledger has been discussed in detail.

Finally, the chapter describes the difference between centralized cloud storage and

blockchain-based distributed cloud storage along with the advantages and disadvan­

tages of applying blockchain for distributed cloud storage. The inclusion of block­

chain in distributed cloud storage preserves the integrity and confidentiality of data,

but the process of collecting digital forensics becomes more complex.

REFERENCES

Aly, M., Khomh, F., Haoues, M., Quintero, A., & Yacout, S. (2019). Enforcing security in

Internet of Things frameworks: A systematic literature review. Internet of Things, 6,

100050. https://doi​.org​/10​.1016​/J​.IOT​.2019​.100050

Battista, G. Di, Donato, V. Di, Patrignani, M., Pizzonia, M., Roselli, V., & Tamassia, R.

(2015). Bitconeview: Visualization of flows in the bitcoin transaction graph. In 2015

IEEE Symposium on Visualization for Cyber Security, VizSec 2015. https://doi​.org​/10​.

1109​/VIZSEC​.2015​.7312773

Beikverdi, A., & Song, J. (2015). Trend of centralization in Bitcoin’s distributed net­

work. In 2015 IEEE/ACIS 16th International Conference on Software Engineering,

Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2015 -

Proceedings. https://doi​.org​/10​.1109​/SNPD​.2015​.7176229

Carnevale, L., Celesti, A., Galletta, A., Dustdar, S., & Villari, M. (2019). Osmotic comput­

ing as a distributed multi-agent system: The body area network scenario. Internet of

Things, 5, 130–139. https://doi​.org​/10​.1016​/J​.IOT​.2019​.01​.001

Dev, J.A. (2014). Bitcoin mining acceleration and performance quantification. Canadian

Conference on Electrical and Computer Engineering. https://doi​.org​/10​.1109​/CCECE​.

2014​.6900989

Dinh, T.T.A., Wang, J., Chen, G., Liu, R., Ooi, B.C., & Tan, K.-L. (2017). BLOCKBENCH:

A framework for analyzing private blockchains. In Proceedings of the ACM SIGMOD

International Conference on Management of Data, Part F127746 (pp. 1085–1100).

https://arxiv​.org​/abs​/1703​.04057v1

Filho, G.P.R., Villas, L.A., Gonçalves, V.P., Pessin, G., Loureiro, A.A.F., & Ueyama, J.

(2019). Energy-efficient smart home systems: Infrastructure and decision-making pro­

cess. Internet of Things, 5, 153–167. https://doi​.org​/10​.1016​/J​.IOT​.2018​.12​.004

Giancaspro, M. (2017). Is a ‘smart contract’ really a smart idea? Insights from a legal perspec­

tive. Computer Law & Security Review, 33(6), 825–835. https://doi​.org​/10​.1016​/J​.CLSR​.

2017​.05​.007