Abstract
Data tampering is one of the most intriguing personal information security concerning issues in online business portals. For various individual or business purposes, clients need to share their personal information with these online business portals. Upon taking conveniences from this sharing of information about an individual, online business sites accumulate client data including client’s most sensitive information for running different data analysis without taking the clients’ authorization. In a view to proposing suggestions, data analysis may need to be done in the online business portals. A recommender system or framework creates an automated personalization on a rundown of items based on the users’ preference of searching any product over the portal. These days, the recommender system or framework is the part and parcel to the online marketing and business portals. However, secure control of client information is missing to some extent in such systems. Blockchain technology guarantees security in data manipulation for the clients in these online portals since it is a secure distributed ledger for storing data transaction. This paper presents a privacy-preserving or privacy-securing platform for recommender framework or system utilizing blockchain technology. The distributed ledger attribute of blockchain gives any client a verified domain where information is utilized for analysis with his/her required consents. Under this platform, clients get rewards (i.e., points, discounts) from the proposed online based company for sharing their information to figure out and propose relevant suggestions.
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Notes
- 1.
We assume that \(ID_g\) is a trusted party of our platform.
- 2.
We assume that TDS is a trusted party of our platform.
- 3.
Users’ permission were taken when they confirm to join our platform.
- 4.
Platform will ask about the permission separately.
References
Al Omar, A., Bhuiyan, M.Z.A., Basu, A., Kiyomoto, S., Rahman, M.S.: Privacy-friendly platform for healthcare data in cloud based on blockchain environment. Future Gener. Comput. Syst. 95, 511–521 (2019)
Al Omar, A., Rahman, M.S., Basu, A., Kiyomoto, S.: MediBchain: a blockchain based privacy preserving platform for healthcare data. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, K.-K.R. (eds.) SpaCCS 2017. LNCS, vol. 10658, pp. 534–543. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-72395-2_49
Azaria, A., Ekblaw, A., Vieira, T., Lippman, A.: MedRec: using blockchain for medical data access and permission management. In: International Conference on Open and Big Data (OBD), pp. 25–30. IEEE (2016)
Cachin, C.: Architecture of the hyperledger blockchain fabric. In: Workshop on Distributed Cryptocurrencies and Consensus Ledgers, vol. 310 (2016)
Davidson, J., et al.: The Youtube video recommendation system. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 293–296. ACM (2010)
Dutta, P., Kumaravel, A.: A novel approach to trust based identification of leaders in social networks. Indian J. Sci. Technol. 9(10) (2016)
Felt, A., Evans, D.: Privacy protection for social networking platforms. In: Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA, 22 May 2008
Frey, R., Wörner, D., Ilic, A.: Collaborative filtering on the blockchain: a secure recommender system for e-commerce. In: Proceedings of the 22nd Americas Conference on Information Systems (AMCIS 2016), San Diego, CA, USA, 11–13 August 2016
Frey, R.M., Vuckovac, D., Ilic, A.: A secure shopping experience based on blockchain and beacon technology. In: Proceedings of the Poster Track of the 10th ACM Conference on Recommender Systems (RecSys 2016), Boston, USA, 17 September 2016 (2016)
Gabison, G.: Policy considerations for the blockchain technology public and private applications. SMU Sci. Tech. L. Rev. 19, 327 (2016)
Gentry, C.: A Fully Homomorphic Encryption Scheme. Stanford University, Stanford (2009)
Goldreich, O.: Secure multi-party computation. Manuscript. Preliminary version, 78 (1998)
Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manag. Inf. Syst. (TMIS) 6(4), 13 (2016)
Hazari, S.S., Mahmoud, Q.H.: A parallel proof of work to improve transaction speed and scalability in blockchain systems. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0916–0921, January 2019
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)
Iwamura, M., Kitamura, Y., Matsumoto, T., Saito, K.: Can we stabilize the price of a cryptocurrency?: Understanding the design of bitcoin and its potential to compete with central bank money (2014)
King, S., Nadal, S.: PPcoin: peer-to-peer crypto-currency with proof-of-stake. Self-published paper, 19 August 2012
Lam, S., Frankowski, D., Riedl, J.: Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. Emerging Trends Inf. Commun. Secur., 14–29 (2006)
Liang, X., Shetty, S., Tosh, D., Kamhoua, C., Kwiat, K., Njilla, L.: ProvChain: a blockchain-based data provenance architecture in cloud environment with enhanced privacy and availability. In: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 468–477. IEEE Press (2017)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008)
Nguyen, C.T., Hoang, D.T., Nguyen, D.N., Niyato, D., Nguyen, H.T., Dutkiewicz, E.:. Proof-of-stake consensus mechanisms for future blockchain networks: fundamentals, applications and opportunities. IEEE Access, 1 (2019)
Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)
Qiu, C., Richard Yu, F., Xu, F., Yao, H., Zhao, C.:. Permissioned blockchain-based distributed software-defined industrial internet of things. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7. IEEE (2018)
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media, Inc. (2015)
Tasnim, M.A., Omar, A.A., Rahman, M.S., Bhuiyan, M.Z.A.: CRAB: blockchain based criminal record management system. In: Wang, G., Chen, J., Yang, L.T. (eds.) SpaCCS 2018. LNCS, vol. 11342, pp. 294–303. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05345-1_25
Chenhan, X., et al.: Making big data open in edges: a resource-efficient blockchain-based approach. IEEE Trans. Parallel Distrib. Syst. 30(4), 870–882 (2018)
Yamamoto, S., Nakao, A.: In-network P2P packet cache processing using scalable P2P network test platform. In: 2011 IEEE International Conference on Peer-to-Peer Computing, pp. 162–163, August 2011
Zyskind, G., Nathan, O., et al.: Decentralizing privacy: using blockchain to protect personal data. In: Security and Privacy Workshops (SPW), 2015 IEEE, pp. 180–184. IEEE (2015)
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This work is partially supported by Institute of Energy, Environment, Research and Development (IEERD), University of Asia Pacific (UAP), Bangladesh.
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Omar, A.A., Bosri, R., Rahman, M.S., Begum, N., Bhuiyan, M.Z.A. (2019). Towards Privacy-preserving Recommender System with Blockchains. In: Wang, G., Bhuiyan, M.Z.A., De Capitani di Vimercati, S., Ren, Y. (eds) Dependability in Sensor, Cloud, and Big Data Systems and Applications. DependSys 2019. Communications in Computer and Information Science, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-15-1304-6_9
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