With the onset of the Information Era and the rapid growth of information technology, ample space for processing and extracting data has opened up. However, privacy concerns may stifle expansion throughout this area. The challenge of reliable mining techniques when transactions disperse across sources is addressed in this study. This work looks at the prospect of creating a new set of three algorithms that can obtain maximum privacy, data utility, and time savings while doing so. This paper proposes a unique double encryption and Transaction Splitter approach to alter the database to optimize the data utility and confidentiality tradeoff in the preparation phase. This paper presents a customized apriori approach for the mining process, which does not examine the entire database to estimate the support for each attribute. Existing distributed data solutions have a high encryption complexity and an insufficient specification of many participants' properties. Proposed solutions provide increased privacy protection against a variety of attack models. Furthermore, in terms of communication cycles and processing complexity, it is much simpler and quicker. Proposed work tests on top of a realworld transaction database demonstrate that the aim of the proposed method is realistic.

翻译：随着信息时代的到来和信息技术的迅猛发展，数据处理与提取领域已开辟出广阔空间。然而，隐私问题可能制约该领域的进一步拓展。本研究针对数据分散在不同数据源时，可靠挖掘技术所面临的挑战展开探讨。本工作旨在构建一套新型的三种算法，这些算法能够在获得最大隐私保护的同时，实现数据效用提升与时间节约。本文提出了一种独特的双重加密和事务拆分器方法，以在预处理阶段对数据库进行改造，从而优化数据效用与机密性之间的权衡。在挖掘过程中，本文提出了一种定制的Apriori方法，该方法无需扫描整个数据库即可估算每个属性的支持度。现有分布式数据解决方案存在加密复杂度高、对多参与方属性描述不足等问题。提出的方案能针对多种攻击模型提供更强的隐私保护。此外，在通信周期与处理复杂度方面，该方法更为简单高效。在真实事务数据库上的实验结果表明，所提方法的目标具有现实可行性。