Privacy Preserving Using Data Mining Systems and Techniques

2005

Abstract Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive information.

2012

Due to the increase in sharing sensitive data through networks among businesses, governments and other parties, privacy preserving has become an important issue in data mining and knowledge discovery. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. This paper proposes a solution for privately computing data mining classification algorithm for horizontally partitioned data without disclosing any information about the sources or the data. The proposed method (PPDM) combines the advantages of RSA public key cryptosystem and homomorphic encryption scheme. Experimental results show that the PPDM method is robust in terms of privacy, accuracy, and efficiency.

The main aim of Data mining techniques are to try to find out helpful patterns from the data that is big in quantity. These ideas or patterns are useful to find out some useful information. The abilities learned through fully knowledge mining approaches may contain confidential information about persons or trade. Upkeep of secrecy is a gigantic aspect of information mining also as a result be taught of attaining some information mining ambitions without dropping the secrecy of the individuals .The assessment of privacy preserving data mining (PPDM) algorithms must don't forget the penalties of those algorithms in mining the outcome along with retaining privacy. Inside the constraints of privateness, a couple of ways have been introduced however nonetheless this branch of exploration is in its early life .The success of privateness preserving data mining procedures is measured in phrases of its efficiency, data utility, degree of uncertainty or resistance to data mining procedures and so on. Nevertheless no privateness maintaining algorithm exists that outperforms all others on all feasible standards. Rather, an algorithm could participate in better than one other on one exact criterion. So, the aim of this paper is to show the current situation of privacy preserving knowledge mining framework and tactics

Asian Journal of Convergence in Technology

Background & Objective: This survey paper examines the application of Federated Learning (FL) and secure Multiparty Computing (MPC) considering medical data privacy, further provides an overview of FL and MPC techniques and discusses their strengths and weaknesses. This also covers other techniques like Homomorphic Encryption, data masking, differential privacy for its efficiency, and limitations. Methods: Eligibility Criteria: For this survey PRISMA[1] framework was employed and popular electronic databases like IEEE, Computers and Security, Bioinformatics, and Google Scholar were scanned along with government websites and research web pages. Papers published between 2018 and 2023 and written in English are considered for the survey. This survey further provides direction towards the future research and potential challenges in the deployment of these techniques at a scale. Results: The search was restricted for the period between 2018 and 2023. Initially, ~100 papers were shortlisted, and after a though review of each paper, finally selected ~35 for this work. However, papers from earlier years are also included as they are found to be relevant for this study. Fig4 below describes the article selection process. Apart from the research papers, government websites are also referred for information on various laws, regulations, and compliance for the privacy of the patient's information. Acts like HIPPA[7], GDPR[8], PDP[9] are reviewed thoroughly to ensure all the aspects are covered to present this survey. Conclusion: The information used in the data mining process comes from data, and such data include personal data about people. Since knowledge is derived from data, the main goal of privacy-preserving data mining is the development of algorithms to conceal or protect certain sensitive information so that it cannot be disclosed to unintended users, hackers, or intruders. Various techniques like homomorphic encryption, differential privacy, data masking, data aggregation, multiparty computation is studied, one single technique may not be sufficient to provide an end-to-end solution. In the future work, we would like to explore on multiple techniques to build a secured ML model.

… International Workshop on Database …, 2009

Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes .So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In recent years, privacy preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. We discuss method for randomization, kanonymization, and distributed privacy preserving data mining. Knowledge is supremacy and the more knowledgeable we are about information break-in, we are less prone to fall prey to the evil hacker sharks of information technology. In this paper, we provide a review of the state-of-the-art methods for privacy and analyze the representative technique for privacy preserving data mining and points out their merits and demerits. Finally the present problems and directions for future research are discussed.

Crossroads, 2009

As it becomes evident, there exists an extended set of application scenarios in which information or knowledge derived from the data must be shared with other (possibly untrusted) entities. The sharing of data and/or knowledge may come at a cost to privacy, primarily due to two reasons:

2015

The Privacy preserving Data mining (PPDM) has been among the important issues of current research that deals with preserving privacy of individual's data over a network. The major area of concern is that non-sensitive data even may deliver sensitive information, including personal information, facts or patterns. In this paper, we present a unique concept of combining different PPDM techniques which provides high level security and integrity to confidential data. This paper mainly highlights the improved results that can be obtained on merging the two different PPDM techniques. One of the latest concept of PPDM called Slicing has also been explained in our paper. It has been observed that slicing preserves better data utility and thus we have tried to merge slicing with one of the best security mechanism that is Cryptography.

2016

With technological revolution, a huge amount of data is being collected and as a consequence the need of mining knowledge from this data is triggered. But, data in its raw form comprises of sensitive information and advances in data mining techniques have increased the privacy breach. However, due to socio-technical transformations, most countries have levied the guidelines and policies for publishing certain data. As a result, a new area known as Privacy Preserving Data Mining (PPDM) has emerged. The goal of PPDM is to extract valuable information from data while retaining privacy of this data. The paper focuses on exploring PPDM in different aspects, such as types of privacy, PPDM scenarios and applications, methods of evaluating PPDM algorithms etc. Also, the paper shows parametric analysis and comparison of different PPDM techniques. The goal of this study is to facilitate better understanding of these PPDM techniques and boost fruitful research in this direction.

International Journal of Computer Science and Telecommunications (IJCST) , 2013

With the extensive amount of data stored in databases and other repositories it is very important to develop a powerful and effective mean for analysis and interpretation of such data for extracting the interesting and useful knowledge that could help in decision making. Data mining is such a technique which extracts the useful information from the large repositories. Knowledge discovery in database (KDD) is another name of data mining. Privacy preserving data mining techniques are introduced with the aim of extract the relevant knowledge from the large amount of data while protecting the sensible information at the same time. In this paper we review on the various privacy preserving data mining techniques like data modification and secure multiparty computation based on the different aspects. We also analyze the comparative study of all techniques followed by the future research work.

International Journal of Computer Applications, 2016

Privacy has become crucial in knowledge based applications. Proper integration of individual privacy is essential for data mining operations. This privacy based data mining is important for sectors like Healthcare, Pharmaceuticals, Research, and Security Service Providers, to name a few. The main categorization of Privacy Preserving Data Mining (PPDM) techniques falls into Perturbation, Secure Sum Computations and Cryptographic based techniques. There exist tradeoffs between privacy preservation and information loss for generalized solutions. The authors of the paper present an extensive survey of PPDM techniques, their classification and give a preliminary implication of technique to be used under specific scenarios.