Privacy Preservation in Federated Market Basket Analysis using Homomorphic Encryption (2024)

Abstract: Our proposed work introduces a novel approach to privacy-preserving federated learning market basket analysis using Homomorphic encryption. By encrypting frequent mining operations using Homomorphic encryption, our method ensures data privacy without compromising analysis efficiency. Experiments on diverse datasets validate its effectiveness in maintaining data integrity while preserving privacy.

## introduction advancements in networking, storage and processing technology have enabled creation of ultra-large databases capable of capturing and storing unprecedented amount of information from diverse users. artificial intelligence (ai) and machine learning (ml) relies heavily on this huge data to efficiently learn, generalize patterns, make accurate predictions, and perform complex tasks. with increased data volume, ml algorithms gains deeper insights into underlying structures of problems, leading to improvement in their performance and reliability. conventional centralized ml model requires sharing private client data with central server for model training, raising significant privacy concerns @xcite due to the sensitive information @xcite . thus, centralized approach raises significant privacy concerns as sensitive information is directly exposed to server. to address this, google in 2016 @xcite introduced federated learning (fl) that enables collaborative training of a global model among multiple nodes without sharing their raw private data. instead, only the model parameters are shared to ensure privacy. figure 1 illustrates the fundamental workflow of federated learning. however, despite these advancements, federated learning still poses privacy risks and challenges @xcite . one significant challenge involves ensuring the security and privacy of local model parameters when they are shared with the central server for analysis. additionally, federated learning requires frequent communication between central server and client devices, resulting in increased communication overhead, particularly when large number of clients are participating. moreover, sharing of local model parameters by clients can attract eavesdroppers or adversaries, potentially intercepting and analyzing the data transmitted between clients and the server. to address aforementioned challenges, various measures have been effectively employed, including anonymization, perturbation, differential privacy and blockchain based methods. in this paper, we leverage the property of homomorphic encryption @xcite within the context of federated market basket analysis, which enables computations to be performed on encrypted data without decryption. the contribution of our study are: ## literature review to protect private data and ensure robust privacy in federated learning, researchers have developed several techniques including anonymization @xcite , differential privacy @xcite ) @xcite , secure multiparty computations @xcite and blockchainbased methods @xcite . association rule mining @xcite , based on diffie-hellman problem along with elliptic curve and digital signature was proposed to improve trustworthiness of data exchange between clients eliminating the trusted third party. however, it faces scalability issue and computational cost for large number of participating clients. two association rule mining @xcite was proposed for horizontally partitioned database. first scheme utilizes elliptic curve cryptosystem that secure the site information and second scheme relies on shamir secret sharing method that effectively addresses the vulnerability against collusion attacks. nevertheless first scheme was susceptible to collusion attack and second was having higher computational cost. svsm @xcite address the challenge of frequent itemset mining in transactional data using local differential privacy. however, scalability issues persist. a centralized fl framework @xcite was designed for mining association rules from electronic healthcare records, ensuring global accuracy while reducing computational cost. fedfpm @xcite is a local differential privacy based approach for mining frequent items efficiently with privacy. ppdmf @xcite proposed for joint venture industrial collaboration for mining of high utility itemsets from multiple datasets without directly sharing the private data. the proposed method results displayed that approach is having high accuracy while preserving privacy. fedfim @xcite and fed-fim_aes uses aes encryption to rapidly mine frequent items along with adding noise in the fed_avg. fl based mining algorithm @xcite considered client server method where clients possess large and diverse datasets, and the server aggregates results from each client. while above techniques aim to protect privacy, they still have some limitations. secure multiparty computation (smc) often involves high communication overhead and intricate key sharing mechanisms. differential privacy presents challenges in selecting an appropriate epsilon value @xcite , impacting the accuracy of mining results. anonymization, although effective, may not always guarantee complete privacy and blockchain in privacy-preserving federated learning (fl) suffers from scalability issues. however, homomorphic encryption offers a promising solution by enabling computations directly on encrypted data without decryption. this minimizes communication overhead, eliminates the need for extensive key sharing, and provides a more efficient and secure approach to privacy-preserving data mining. ## conclusion and future work the proposed methodology explores utilization of federated learning combined with homomorphic encryption for market basket analysis, frequent itemset mining, and product recommendation. by employing the apriori and fp-growth algorithms on transactional datasets, frequent itemsets, association rules, and product recommendations were efficiently extracted. homomorphic encryption was then applied to ensure the confidentiality and integrity of client results during transmission to the server for training the global model, thereby preserving privacy. additionally, analysis using metrics such as entropy, mutual information, and kl divergence indicated that the data remained closely aligned with the original after encryption, unlike some other methods. furthermore, the encryption and decryption time were minimal, and computational complexities were reduced, as homomorphic encryption allows computations to be performed without decryption and does not require excessive data transmission from client to server. for future work, exploring other variants of homomorphic encryption schemes on alternative frequent mining algorithms could be beneficial.