In this era of Internet, the exchange of data between the users and service providers has grown tremendously. Organizations in health, banking, social network, criminal and government sectors have been collecting and processing the individuals’ information for their gainful purpose. However, collecting and sharing of the individuals’ information which could be sensitive and confidential, for data mining may cause a breach in data privacy. In many applications, selective data collection of confidential and sensitive information of the users’ needs to be modified for preserving it from unauthorized access and disclosure. Many data mining techniques that include statistical, k-anonymity, cryptographic, perturbation and randomization methods, etc. have been evolved for protecting and preserving data privacy. These techniques have their own limitations, it may be the case that the privacy protection is adequate or computations complexities are high and expensive. To address the limitations of the above-mentioned techniques, a methodology comprising of encoding and randomization, is proposed to preserve privacy. This technique called as Randomized Encoding (RE) technique, in which encoding is performed with addition of random noise from a known distribution to the original data for perturbing the data before its release to the public domain. The core component of this technique is a novel primitive of using Randomized Encoding (RE) which is quite similar to the spirit of other cryptographic algorithms. The reconstruction of an approximation to the original data distribution is done from the perturbed data and used for data mining purposes. There is always a trade-off between information loss and privacy preservation. To achieve balance between privacy and data utility, the dataset attributes are first classified into sensitive and quasi-identifiers. The pre-classified confidential and sensitive data attributes are perturbed using Base 64 encoding with addition of a randomly generated noise for preserving privacy. In this variable dynamic proposed approach, the result analysis of the experiment conducted suggests that the proposed technique performs computationally efficient and preserves privacy while adequately maintaining data utility in comparison with other privacy preserving techniques such as anonymization approach.

在这个互联网时代，用户和服务提供商之间的数据交换急剧增长。卫生、银行、社交网络、犯罪和政府部门的组织一直在收集和处理个人信息，以谋取利益。但是，为数据挖掘收集和共享可能是敏感和机密的个人信息可能会导致数据隐私泄露。在许多应用中，需要修改用户机密和敏感信息的选择性数据收集，以防止未经授权的访问和泄露。许多数据挖掘技术，包括统计、k-匿名、密码学、扰动和随机化方法等，已经发展到保护和维护数据隐私。这些技术都有自己的局限性，可能是隐私保护充分或计算复杂度高且成本高的情况。为了解决上述技术的局限性，提出了一种包括编码和随机化的方法来保护隐私。这种技术称为随机编码 (RE) 技术，在该技术中，通过将来自已知分布的随机噪声添加到原始数据中来执行编码，以便在数据发布到公共领域之前扰乱数据。该技术的核心组件是使用随机编码 (RE) 的新颖原语，这与其他密码算法的精神非常相似。对原始数据分布的近似重建是从扰动的数据中完成的，并用于数据挖掘目的。在信息丢失和隐私保护之间总是需要权衡。为了在隐私和数据效用之间取得平衡，首先将数据集属性分为敏感和准标识符。预先分类的机密和敏感数据属性使用 Base 64 编码进行扰动，并添加随机生成的噪声以保护隐私。在这种可变动态提议的方法中，所进行的实验结果分析表明，与其他隐私保护技术（如匿名化方法）相比，所提出的技术在计算上高效并保护隐私，同时充分保持数据效用。预先分类的机密和敏感数据属性使用 Base 64 编码进行扰动，并添加随机生成的噪声以保护隐私。在这种可变动态提议的方法中，所进行的实验结果分析表明，与其他隐私保护技术（如匿名化方法）相比，所提出的技术在计算上高效并保护隐私，同时充分保持数据效用。预先分类的机密和敏感数据属性使用 Base 64 编码进行扰动，并添加随机生成的噪声以保护隐私。在这种可变动态提议的方法中，所进行的实验结果分析表明，与其他隐私保护技术（如匿名化方法）相比，所提出的技术在计算上高效并保护隐私，同时充分保持数据效用。