Data mining is a key technology in big data analytics and it can discover understandable knowledge (patterns) hidden in large data sets. Association rule is one of the most useful knowledge patterns, and a large number of algorithms have been developed in the data mining literature to generate association rules corresponding to different problems and situations. Privacy becomes a vital issue when data mining is used to sensitive data sets like medical records, commercial data sets and national security. In this Letter, we present a quantum protocol for mining association rules on vertically partitioned databases. The quantum protocol can improve the privacy level preserved by known classical protocols and at the same time it can exponentially reduce the computational complexity and communication cost.
Data mining is a key technology in big data analytics and it can discover understandable knowledge (patterns) hidden in large data sets. Association rule is one of the most useful knowledge patterns, and a large number of algorithms have been developed in the data mining literature to generate association rules corresponding to different problems and situations. Privacy becomes a vital issue when data mining is used to sensitive data sets like medical records, commercial data sets and national security. In this Letter, we present a quantum protocol for mining association rules on vertically partitioned databases. The quantum protocol can improve the privacy level preserved by known classical protocols and at the same time it can exponentially reduce the computational complexity and communication cost.
Data analytics (such as association rule mining and decision tree mining) can discover useful statistical knowledge from a big data set. But protecting the privacy of the data provider and the data user in the process of analytics is a serious issue. Usually, the privacy of both parties cannot be fully protected simultaneously by a classical algorithm. In this paper, we present a quantum protocol for data mining that can much better protect privacy than the known classical algorithms: (1) if both the data provider and the data user are honest, the data user can know nothing about the database except the statistical results, and the data provider can get nearly no information about the results mined by the data user; (2) if the data user is dishonest and tries to disclose private information of the other, she/he will be detected with a high probability; (3) if the data provider tries to disclose the privacy of the data user, she/he cannot get any useful information since the data user hides his privacy among noises.
Association rules mining (ARM) is one of the most important problems in knowledge discovery and data mining. Given a transaction database that has a large number of transactions and items, the task of ARM is to acquire consumption habits of customers by discovering the relationships between itemsets (sets of items). In this paper, we address ARM in the quantum settings and propose a quantum algorithm for the key part of ARM, finding frequent itemsets from the candidate itemsets and acquiring their supports. Specifically, for the case in which there are Mf(k) frequent k-itemsets in the Mc(k) candidate k-itemsets (${M}_{f}^{(k)}\ensuremath{\le}{M}_{c}^{(k)}$), our algorithm can efficiently mine these frequent k-itemsets and estimate their supports by using parallel amplitude estimation and amplitude amplification with complexity $O(\frac{k\sqrt{{M}_{c}^{(k)}{M}_{f}^{(k)}}}{\ensuremath{\epsilon}})$, where $\ensuremath{\epsilon}$ is the error for estimating the supports. Compared with the classical counterpart, i.e., the classical sampling-based algorithm, whose complexity is $O(\frac{k{M}_{c}^{(k)}}{{\ensuremath{\epsilon}}^{2}})$, our quantum algorithm quadratically improves the dependence on both $\ensuremath{\epsilon}$ and Mc(k) in the best case when ${M}_{f}^{(k)}\ensuremath{\ll}{M}_{c}^{(k)}$ and on $\ensuremath{\epsilon}$ alone in the worst case when ${M}_{f}^{(k)}\ensuremath{\approx}{M}_{c}^{(k)}$.
Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.
The rapid development of big data puts forward higher requirements on computational efficiency and storage capacity. But the traditional mining algorithms based on classical computing were unable to satisfy the demand of data analysis and computing. Because of quantum systems unique ability of hyper parallel computation and hyper acceleration, the large-scale computing and data storage can be solve well. Although the development of quantum computer has not yet achieved a qualitative leap, quantum computing in big data application has achieved lots of theoretical achievements. In this paper, we make quantum data mining as a starting point. Since we have no sufficient space to store candidate sets of association rules and have low computation ability in the processing of association rules, we propose a novel quantum method of data storage and retrieval for association rules mining based on Boolean matrix, we propose the algorithm called Q-Eclat can accelerate the computation of candidate sets support. According to our analysis based on open data source, the proposed method outperforms the classical Eclat algorithm in terms of storage capacity and computing ability.
Cloud computing is a powerful and popular information technology paradigm that enables data service outsourcing and provides higher-level services with minimal management effort. However, it is still a key challenge to protect data privacy when a user accesses the sensitive cloud data. Privacy-preserving database query allows the user to retrieve a data item from the cloud database without revealing the information of the queried data item, meanwhile limiting user’s ability to access other ones. In this study, in order to achieve the privacy preservation and reduce the communication complexity, a quantum-based database query scheme for privacy preservation in cloud environment is developed. Specifically, all the data items of the database are firstly encrypted by different keys for protecting server’s privacy, and in order to guarantee the clients’ privacy, the server is required to transmit all these encrypted data items to the client with the oblivious transfer strategy. Besides, two oracle operations, a modified Grover iteration, and a special offset encryption mechanism are combined together to ensure that the client can correctly query the desirable data item. Finally, performance evaluation is conducted to validate the correctness, privacy, and efficiency of our proposed scheme.
This paper presents a novel privacy computation protocol leveraging Quantum Key Distribution (QKD) for enhanced data privacy. The core idea is to utilize the principles of QKD to generate a shared secret key, which is then employed to perform computations on encrypted data, ensuring the confidentiality of the results. This approach addresses the fundamental challenge of traditional computation, where data is vulnerable to interception and manipulation during processing. The protocol details the key management process, the encryption/decryption scheme based on QKD, and the computation steps undertaken using the shared key. The proposed system offers a significant advancement in privacy-preserving computation, particularly in scenarios where data sensitivity is paramount. The design focuses on minimizing the computational overhead associated with QKD while maximizing the level of privacy protection. This work lays the groundwork for future research and development in privacy-preserving quantum computing and communication.