Privacy Preserving Data Mining Algorithms Research Articles

Privacy preserving data mining - past and present

Data mining is the process of discovering patterns and correlations within the huge volume of data to forecast the outcomes. There are serious challenges occurring in data mining techniques due to privacy violation and sensitive information disclosure while providing the dataset to third parties. It is necessary to protect user's private and sensitive data from exposure without the authorisation of data holders or providers when extracting useful information and revealing patterns from the dataset. Also, internet phishing gives more threat over the web on extensive spread of private information. Privacy preserving data mining (PPDM) is an essential for exchanging confidential information in terms of data analysis, validation, and publishing. To achieve data privacy, a number of algorithms have been designed in the data mining sector. This article delivers a broad survey on privacy preserving data mining algorithms, different datasets used in the research and analyses the techniques based on certain parameters. The survey is highlighted by identifying the outcome of each research along with its advantages and disadvantages. This survey will guide the future research in PPDM to choose the appropriate techniques for their research.

Privacy preservation of cloud data in business application enabled by multi-objective red deer-bird swarm algorithm

Decision-making is one of the important deals in knowledge transfer and it can be useful for the multi-source domains. Meanwhile, the existing knowledge transfer schemes do not use privacy-preserving techniques for preserving security. This can be a problem for critical domains like financial market forecasting as the misuse of security can lead to legal and financial implications. In recent years, cloud services have revolutionized various technological applications. Cloud computing has become more popular with digital technologies as it provides uninterrupted services like transmission, storage, and intensive computing of data. The architecture of the cloud is also cost-efficient. Besides, various promising services from the cloud, some challenges need to be addressed to secure the privacy of the cloud users as millions of users access its services. Privacy preservation is an important aspect in the field of data mining, and the necessity of securing important data in the cloud from hackers is on the rise. Privacy-preserving data mining algorithms have been analyzed over recent years to provide sufficient solutions for securing the privacy of the data in the cloud. This paper plans to introduce a new hybrid meta-heuristic concept for developing a privacy preservation strategy towards business data under the cloud sector. The main objective of this paper is to design a new hybrid red deer-bird swarm algorithm (RD-BSA) to ensure higher convergence and since the use of control parameters over the solution generation is minimized. The proposed privacy preservation scheme on three financial databases is evaluated with the performance against the existing privacy preservation schemes. Different analyses like statistical, key sensitivity, Known-Plaintext Attack (KPA), and Chosen-Plaintext Attack (CPA) are used for evaluating the efficiency of the algorithm. The comparative analysis of the proposed model over the conventional models demonstrates its effective performance via diverse analysis.

A SURVEY OF PRIVACY PRESERVING DATA MINING ALGORITHMS

By Abou el ela Abdou Hussein, Nermin Hamza & 2 more. A wide survey of different privacy preserving data mining (PPDM) algorithms and analyses of the representative techniques for PPDM, and points out their merits and demerits.

Anonymized noise addition in subspaces for privacy preserved data mining in high dimensional continuous data

Data privacy is a major concern in data mining. Privacy-preserving data mining algorithms have been used for preserving privacy in data mining. However, privacy-preserving data mining on high dimensional continuous data leads to high data loss, information loss and identifying clusters are very difficult. In this paper, a novel technique Anonymized Noise Addition in Subspaces (ANAS) is proposed, which reduces data loss, information loss and enhances identification of clusters and privacy. Anonymization using aggregation is performed in dense and non-dense subspaces considering Euclidean distances to reduce data loss and enhance privacy. Random noise within the subspace limits is then applied to anonymized subspaces to enhance identification of clusters and reduce data loss. ANAS is run on benchmark datasets, and results show that ANAS can identify 80% of the original dataset clusters on sparse datasets, whereas the existing techniques do not identify any clusters. ANAS reduces data loss by 50%, information loss by 20% and enhances privacy by 40%.

PPDM-TAN: A Privacy-Preserving Multi-Party Classifier

Distributed medical, financial, or social databases are analyzed daily for the discovery of patterns and useful information. Privacy concerns have emerged as some database segments contain sensitive data. Data mining techniques are used to parse, process, and manage enormous amounts of data while ensuring the preservation of private information. Cryptography, as shown by previous research, is the most accurate approach to acquiring knowledge while maintaining privacy. In this paper, we present an extension of a privacy-preserving data mining algorithm, thoroughly designed and developed for both horizontally and vertically partitioned databases, which contain either nominal or numeric attribute values. The proposed algorithm exploits the multi-candidate election schema to construct a privacy-preserving tree-augmented naive Bayesian classifier, a more robust variation of the classical naive Bayes classifier. The exploitation of the Paillier cryptosystem and the distinctive homomorphic primitive shows in the security analysis that privacy is ensured and the proposed algorithm provides strong defences against common attacks. Experiments deriving the benefits of real world databases demonstrate the preservation of private data while mining processes occur and the efficient handling of both database partition types.

Subspace-based aggregation for enhancing utility, information measures, and cluster identification in privacy preserved data mining on high-dimensional continuous data

ABSTRACT Clustering is a data mining technique that has been effectively used in the last few decades for knowledge extraction. Privacy is a major problem while releasing data for clustering and therefore privacy-preserving data mining (PPDM) algorithms have been developed. Aggregation is a popular PPDM technique that has been used. However, in the last few years, certain applications require that data mining be performed on high-dimensional data. The present privacy preservation techniques perform aggregation in a univariate manner along each dimension. This affects the utility measures, information measures, and especially retention of original clusters. This paper proposes a new technique called as subspace-based aggregation (SBA). SBA categorizes the dimensions into dense and non-dense subspaces based on the density of points. Aggregation is performed separately for dense and non-dense subspaces. This approach helps to maximize utility measures, information measures, and retention of clusters. SBA is run on high-dimensional continuous datasets from UCI Machine Learning repository. SBA is compared with related work methods such as SINGLE, SIMPLE, MDAV, and PPPCA. SBA provides an improvement of 66% in utility, 400% in cluster identification, 5% in co-variance, and standard deviation.

An evolutionary feature set decomposition based anonymization for classification workloads: Privacy Preserving Data Mining

Privacy has become an important concern while publishing micro data about a population. The emerging area called privacy preserving data mining (PPDM) focus on individual privacy without compromising data mining results. An adversarial exploitation of published data poses a risk of information disclosure about individuals. On the other hand, imposing privacy constraints on the data results in substantial information loss and compromises the legitimate data analysis. Motivated by the increasing growth of PPDM algorithms, we first investigate the privacy implications and the crosscutting issues between privacy versus utility of data. We present a privacy model that embeds the anonymization procedure in to a learning algorithm and this has mitigated the additional overheads imposed on data mining tasks. Our primary concern about PPDM is that the utility of data should not be compromised by the transformation applied. Different data mining classification workloads are analyzed with the proposed anonymization procedure for any side effects incurred. It is shown empirically that classification accuracy obtained for most of the datasets outperforms the results obtained with original dataset.

A Survey on Privacy Preserving Data mining

Data mining objectives to seek out valuable patterns from big quantity of data. These patterns signify information and are conveyed in clusters or organization ideas. 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.

Efficient hiding of confidential high-utility itemsets with minimal side effects

Privacy preserving data mining (PPDM) is an emerging research problem that has become critical in the last decades. PPDM consists of hiding sensitive information to ensure that it cannot be discovered by data mining algorithms. Several PPDM algorithms have been developed. Most of them are designed for hiding sensitive frequent itemsets or association rules. Hiding sensitive information in a database can have several side effects such as hiding other non-sensitive information and introducing redundant information. Finding the set of itemsets or transactions to be sanitised that minimises side effects is an NP-hard problem. In this paper, a genetic algorithm (GA) using transaction deletion is designed to hide sensitive high-utility itemsets for PPUM. A flexible fitness function with three adjustable weights is used to evaluate the goodness of each chromosome for hiding sensitive high-utility itemsets. To speed up the evolution process, the pre-large concept is adopted in the designed algorithm. It reduces the number of database scans required for verifying the goodness of an evaluated chromosome. Substantial experiments are conducted to compare the performance of the designed GA approach (with/without the pre-large concept), with a GA-based approach relying on transaction insertion and a non-evolutionary algorithm, in terms of execution time, side effects, database integrity and utility integrity. Results demonstrate that the proposed algorithm hides sensitive high-utility itemsets with fewer side effects than previous studies, while preserving high database and utility integrity.

An Efficient Clustering for Anonymizing Data and Protecting Sensitive Labels

Sharing data to a third party for the mutual benefit of the data owners plays a vital need in all computer applications. Ensuring the privacy of sensitive data is an important issue in data sharing and hence there is a need for privacy preserving data mining algorithms. Traditional privacy preserving algorithms are prone to attacks, have high information loss and sometimes fail to achieve the privacy constraints. In this paper, a (G,S) clustering algorithm is proposed to achieve clusters each with S diverse sensitive values for the sensitive attribute. The number of distinct sensitive values in each cluster would be exactly equal to the number of sensitive values in the given data set. The clusters’ formed are anonymized by replacing the actual values with the centroid(G) of the cluster. The algorithm achieves the maximum possible diverse sensitive values in each cluster making it resilient to similarity attack. The method guarantees high degree of privacy with very minimal loss of information. The privacy degree of the clusters formed is at least equal to the number of distinct values in the sensitive attribute. The worst case linking probability would be as low as 0.5. The significance of this algorithm is its resilience to similarity attack. The performance of the algorithm is proved to be better than the existing algorithms in terms of KL Divergence, Discernibility metric and few other metrics.

Femtocell Interference and Probability of Connection in different Areas and Various SINR Threshold Values at Downlink

Many privacy-preserving data mining algorithms attempt to hide what database owners consider as sensitive. Specifically, in the association rules domain, many of these algorithms are based on item restriction methods; that is, removing items from some transactions in order to hide sensitive frequent itemsets. There are two known algorithms for that purpose, ISL (Increase Support of Left) and DSR (Decrease Support of Right). Both of them make use of user-specified values for minimum support threshold and minimum confidence threshold as input. Since ISL and DSR techniques aim at hiding all sensitive rules, they cannot avoid the undesired side effects. In this paper, a new algorithm for hiding sensitive rules is proposed based on ISL and DSR. It depends on decreasing the confidence of the sensitive rule by dealing with both Left Hand Side (LHS) and Right Hand Side (RHS) according to the ratio between them. This technique introduces introduce a reasonable side effect (lost and new rules) as compared with ISL and DSR algorithms which introduces a high number of new rules and/or lost rules. Experimental results have shown that the proposed algorithm effectively reduces the side effects which occur due to the hide operation when compared with the ISL and DSR algorithms and that it gives a good hiding ratio for the sensitive rules.

Privacy Preserving Using Association Rule in Data Mining Techniques

This paper describes the problem of Privacy Preserving Data Mining (PPDM). It describes some of the common cryptographic tools and constructs used in several PPDM techniques. The paper describes an overview of some of the well known PPDM algorithms, - ID3 for decision tree, association rule mining, EM clustering, frequency mining and Naive Bayes. Most of these algorithms are usually a modification of a well known data mining algorithm along with some privacy preserving techniques. The paper finally describes the problem of using a model without knowing the model rules on context of passenger classification at the airlines security checkpoint by homeland security. This paper is intended to be a summary and a high level overview of PPDM.

A Survey: Privacy Preservation Techniques in Data Mining

Data mining tools aims to find useful patterns from large amount of data. These patterns represent information and are conveyed in decision trees, clusters or association rules. The knowledge discovered by various data mining techniques may contain private information about people or business. Preservation of privacy is a significant aspect of data mining and thus study of achieving some data mining goals without losing the privacy of the individuals’ .The analysis of privacy preserving data mining (PPDM) algorithms should consider the effects of these algorithms in mining the results as well as in preserving privacy. Within the constraints of privacy, several methods have been proposed but still this branch of research is in its formative years .The success of privacy preserving data mining algorithms is measured in terms of its performance, data utility, level of uncertainty or resistance to data mining algorithms etc. However no privacy preserving algorithm exists that outperforms all others on all possible criteria. Rather, an algorithm may perform better than another on one specific criterion. So, the aim of this paper is to present current scenario of privacy preserving data mining framework and techniques.

Enhanced Batch Generation based Multilevel Trust Privacy Preserving in Data Mining

motivation of Privacy Preserving Data Mining (PPDM) is to obtain valid data mining results without access to the original sensitive information. The different privacy preserving technique on Perturbation based PPDM approach introduces random perturbation to individual values to preserve privacy before data are published. This proposed work is based on perturbation based privacy preserving data mining. Here random perturbation approach is applied to provide privacy on the data set. Previously privacy is limited to single level trust in providing privacy to the data but now it is enhanced to multi level trust. The problem with existing multi level trust PPDM algorithms is that they fail to protect form non linear attacks. Considering that this proposed work make uses enhanced batch generation to provide privacy in the multi level trust in which data will perturb multiple times so that it can avoid non linear attacks.

Differentially private feature selection under MapReduce framework

Privacy preserving data mining algorithms are crucial for the personal data analysis, such as medical and financial records. This paper focuses on feature selection and proposes a new privacy preserving distributed algorithm, which can effectively select features based on differential privacy and Gini index under the MapReduce framework. At the same time, the theoretic analysis for privacy guarantee is also presented. Some experiments are conducted on bench-mark datasets, the simulation results indicate that during the selection of important features, the proposed algorithm can preserve privacy information to a certain extent with less time cost than on centralized counterpart.

Privacy-Preserving Data Exploration in Genome-Wide Association Studies.

Genome-wide association studies (GWAS) have become a popular method for analyzing sets of DNA sequences in order to discover the genetic basis of disease. Unfortunately, statistics published as the result of GWAS can be used to identify individuals participating in the study. To prevent privacy breaches, even previously published results have been removed from public databases, impeding researchers' access to the data and hindering collaborative research. Existing techniques for privacy-preserving GWAS focus on answering specific questions, such as correlations between a given pair of SNPs (DNA sequence variations). This does not fit the typical GWAS process, where the analyst may not know in advance which SNPs to consider and which statistical tests to use, how many SNPs are significant for a given dataset, etc. We present a set of practical, privacy-preserving data mining algorithms for GWAS datasets. Our framework supports exploratory data analysis, where the analyst does not know a priori how many and which SNPs to consider. We develop privacy-preserving algorithms for computing the number and location of SNPs that are significantly associated with the disease, the significance of any statistical test between a given SNP and the disease, any measure of correlation between SNPs, and the block structure of correlations. We evaluate our algorithms on real-world datasets and demonstrate that they produce significantly more accurate results than prior techniques while guaranteeing differential privacy.

Privacy-Preserving Data Mining Algorithm Quantum Ant Colony Optimization

Bayesian network has been used extensively in data mining. The Privacy-Preserving data mining algorithm based on quantum ant colony optimization is proposed in this paper. The algorithm is based on distributed database. The algorithm is divided into two steps. In the first step, the modified quantum ant colony optimization algorithm is used to get the local Bayesian network structure. The purpose of the second step is getting the global Bayesian network structure by using local ones. In order to protect the privacy, the secure sum is used in the algorithm. The algorithm is proved to be convergent on theory. Some experiments have been done on the algorithm, and the results proved that the algorithm is feasible.

Privacy preserving perceptron learning in malicious model

Privacy preserving data mining algorithms are proposed to protect the participating parties’ data privacy in data mining processes. So far, most of these algorithms only work in the semi-honest model that assumes all parties follow the algorithms honestly. In this paper, we propose two privacy preserving perceptron learning algorithms in the malicious model, for horizontally and vertically partitioned data sets, respectively. So far as we know, our algorithms are the first perceptron learning algorithms that can protect data privacy in the malicious model.

Fully homomorphic encryption based two-party association rule mining

Association rule mining (ARM) is one of the popular data mining methods that discover interesting correlations amongst a large collection of data, which appears incomprehensible. This is known to be a trivial task when the data is owned by one party. But when multiple data sites collectively engage in ARM, privacy concerns are introduced. Due to this concern, privacy preserving data mining algorithms have been developed to attain the desired result, while maintaining privacy. In the case of two party privacy preserving ARM for horizontally partitioned databases, both parties are required to compare their itemset counts securely. This problem is comparable to the famous millionaire problem of Yao. However, in this paper, we propose a secure comparison technique using fully homomorphic encryption scheme that provides a similar level of security to the Yao based solution, but promotes greater efficiency due to the reuse of resources.

Privacy preserving distributed knowledge discovery: survey and future directions

The aim of privacy preserving data mining algorithms is to extract relevant knowledge from large amounts of data while protecting at the same time sensitive information. In the first part of this paper, we discuss privacy preserving distributed data mining techniques and provide a survey on the state–of–the–art methods in this field. We have considered randomisation, k–anonymisation, and cryptographic techniques. In the second part of the paper, we have described our proposed system for privacy preserving knowledge discovery over distributed databases which is still under development phase. The system is designed to perform local operations (local mining) in each site. This produces intermediate data that can be used to obtain the final result without revealing the private information of any site. Our proposal is mainly based on association rule mining and cryptographic techniques.