By: Himanshu Tiwari, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, email@example.com
Online dating has grown, but data security, privacy, and authenticity issues sometimes overshadow it. This study presents a new blockchain-federated learning approach for online dating services. This synergy addresses fraud, data breaches, and user privacy concerns by creating a secure, transparent, and privacy-preserving environment. We explore how blockchain’s immutability and federated learning’s privacy-preserving intelligence might transform online dating environments.
Online dating platforms have revolutionized interpersonal relationships in the digital era, offering opportunities to connect beyond traditional social circles. However, these platforms face significant challenges, including data privacy, verification of user authenticity, and the security of sensitive information. This research introduces an innovative framework combining blockchain and federated learning, aiming to enhance trust, security, and privacy within online dating environments .
Background and Related Work:
2.1. Blockchain in Secure Social Platforms:
Blockchain technology, known for underpinning cryptocurrencies, has applications extending to secure social interactions online. Its decentralized and immutable characteristics ensure transparency and trust, crucial for sensitive personal interactions in online dating scenarios. However, its application in the online dating sphere remains largely unexplored.
2.2. Federated Learning for Privacy-Preserving Data Analysis:
Federated learning involves training machine learning models across multiple devices, holding local data samples, without exchanging them. This technique is pivotal in environments handling sensitive data, such as online dating platforms, ensuring personalized experiences without compromising user privacy.
We propose an online dating platform architecture that employs blockchain for the secure management of user profiles, interactions, and transactions, alongside federated learning to enable intelligent matchmaking and personalized user experiences without central data accumulation.
3.1. System Architecture:
The system is grounded in a blockchain network, where users’ profiles and interactions are securely stored and verified, ensuring authenticity and transparency. The federated learning mechanism operates concurrently, analysing user preferences and behaviour from data localized on user devices, contributing to a global learning model that continually enhances matchmaking algorithms.
3.2. Interaction Workflow:
When users interact with the platform, their data and activities are recorded as transactions on the blockchain, ensuring data integrity and traceability. The federated learning algorithm, meanwhile, analyzes on-device data to understand user preferences, improving its recommendations. This dual-process ensures a secure and personalized user experience.
Security and Privacy Analysis:
The integration of blockchain and federated learning addresses core security and privacy concerns. Blockchain’s transparency and immutability reduce fraudulent profiles and activities, building user trust. Federated learning, in contrast, enhances user privacy by decentralizing data processing, preventing sensitive data from being stored or misused by the platform or third parties.
Challenges and Future Work:
Despite its potential, the proposed system faces challenges, including the scalability of blockchain components, potential latency in federated learning processes, and ensuring user anonymity while maintaining data authenticity. Future research directions include exploring scalable blockchain solutions, advanced federated learning algorithms for real-time analytics, and robust anonymization techniques that comply with global data protection regulations.
This article presents a pioneering approach to online dating that leverages the strengths of blockchain and federated learning. While challenges need addressing, the proposed system marks a substantial step towards a more secure and private online dating experience, potentially setting a new standard for social platforms in the digital age.
- Li L, Fan Y, Tse M, Lin KY. A review of applications in federated learning. Computers & Industrial Engineering. 2020 Nov 1;149:106854.
- Mammen PM. Federated learning: Opportunities and challenges. arXiv preprint arXiv:2101.05428. 2021 Jan 14.
- Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, Jin S, Quek TQ, Poor HV. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security. 2020 Apr 17;15:3454-69.
- Guidi B. When blockchain meets online social networks. Pervasive and Mobile Computing. 2020 Feb 1;62:101131.
- Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R, Zhou Y. A hybrid approach to privacy-preserving federated learning. InProceedings of the 12th ACM workshop on artificial intelligence and security 2019 Nov 11 (pp. 1-11).
- Bhatti, M. H., Khan, J., Khan, M. U. G., Iqbal, R., Aloqaily, M., Jararweh, Y., & Gupta, B. (2019). Soft computing-based EEG classification by optimal feature selection and neural networks. IEEE Transactions on Industrial Informatics, 15(10), 5747-5754.
- Sahoo, S. R., & Gupta, B. B. (2019). Hybrid approach for detection of malicious profiles in twitter. Computers & Electrical Engineering, 76, 65-81.
- Gupta, B. B., Yadav, K., Razzak, I., Psannis, K., Castiglione, A., & Chang, X. (2021). A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment. Computer Communications, 175, 47-57.
- Cvitić, I., Perakovic, D., Gupta, B. B., & Choo, K. K. R. (2021). Boosting-based DDoS detection in internet of things systems. IEEE Internet of Things Journal, 9(3), 2109-2123.
Tiwari H. (2023) Enhancing Trust and Privacy in Online Dating: A Novel Approach Using Blockchain and Federated Learning, Insights2Techinfo, pp.1