Privacy Preserving Data Mining Research Articles

Big Data and Privacy with a focus on Statistical Approaches to Ensuring Data Confidentiality

In the era of Big Data, where vast datasets fuel innovation across industries, the paramount concern remains safeguarding individual privacy. This article explores how statistical approaches ensure data confidentiality amidst the proliferation of digital information. Differential privacy techniques introduce calibrated noise to protect identities while preserving data utility, crucial for compliance and trust in data-driven decision-making. Secure Multiparty Computation (MPC) enables collaborative analysis without exposing raw data, supporting privacy in sectors like healthcare and finance. Privacy-preserving data mining techniques integrate encryption and anonymization to extract insights while shielding sensitive information. Anonymization and de-identification methods further bolster privacy by masking identifiable data, essential for adhering to stringent regulations like GDPR and HIPAA. As data generation escalates, advancing these statistical methods is essential for maintaining privacy integrity in the evolving landscape of Big Data applications. Keywords: Big Data, Privacy, Statistical approaches, Differential privacy, Secure multiparty computation.

New approach for efficient malicious multiparty private set intersection

The Multiparty Private Set Intersection (MPSI) protocol allows untrusted parties to compute the intersection of their sets without revealing any additional information. MPSI has found wide applications in fields such as advertising, vertical federated learning, privacy-preserving data mining, and private contact tracing. However, existing MPSI solutions face challenges in terms of security and efficiency. Some MPSI schemes are secure only against semi-honest adversaries and cannot effectively defend against malicious adversaries. Other schemes, while capable of withstanding malicious adversaries, exhibit efficiency problems, especially as the number of corrupt participants increases. A practical MPSI solution must maintain high security and efficiency, even when handling large-scale data and a majority of corrupted participants.This paper introduces a novel MPSI scheme named TH-PSI, built upon a new star-shaped topological network communication framework based on the trusted execution environment. TH-PSI has been rigorously proven to be secure within the standard Universal Composability framework, capable of thwarting malicious adversaries. To demonstrate the efficiency of TH-PSI, we conduct a comprehensive series of experiments. We implement the TH-PSI scheme and tested it in both LAN and WAN settings, involving up to 30 participants and datasets as large as 222. We compare TH-PSI with state-of-the-art MPSI schemes, ENOC21 and NTY21, with a specific focus on their efficiency in the presence of malicious adversaries. Our experiments show that TH-PSI is an efficient solution capable of delivering results within a matter of seconds, even when dealing with data at the million-level and a majority of corrupted participants.

An end-to-end knowledge graph solution to the frequent itemset hiding problem

The Frequent Itemset Hiding Problem (FIHP) constitutes a critical aspect of Privacy-Preserving Data Mining, aiming to protect sensitive information while extracting valuable patterns. Despite the ubiquity of Knowledge Graphs (KGs) in many domains, their integration with FIHP remains an underexplored research area. In this paper, we present a new solution to FIHP, leveraging graph-based algorithms and Community Detection. Our method employs the efficiency and structure of KGs to quickly locate what needs to be hidden in order to strategically conceal sensitive knowledge within the data, while ensuring the preservation of KG structure and utility. Through comprehensive evaluations on established FIHP datasets, we showcase the efficacy of our approach in terms of processing time and increased data privacy. The results imply that our proposed method holds promise for facilitating privacy-preserving KG analytics in real-world applications, particularly in scenarios involving substantial streaming data, where existing approaches encounter limitations.

Retraction Note: An improved privacy-preserving data mining technique using singular value decomposition with three-dimensional rotation data perturbation

Retraction Note: An improved privacy-preserving data mining technique using singular value decomposition with three-dimensional rotation data perturbation

Privacy preserving rare itemset mining

In recent years, rare pattern mining has shown great vitality in some real-world fields, such as disease diagnosis, criminal behavior analysis, anomaly detection in networks, and so on. When data organizations publish or share information publicly, shared data can be at risk of leakage as data mining techniques may discover sensitive knowledge and information. To keep competitors from obtaining hidden information after processing the database, privacy-preserving data mining (PPDM) has been proposed and studied widely. However, most of the techniques in PPDM are applied to frequent pattern mining and cannot deal with the privacy protection problems in rare pattern mining, such as network vulnerability detection and abnormal medical data. To address this limitation, we introduce a privacy-preserving technique for rare pattern mining. In this paper, two novel algorithms named Longest Transaction-Minimum Item Number (LT-MIN) and Longest Transaction-Maximum Item Number (LT-MAX) are proposed to hide sensitive rare itemsets and return the sanitized database. These two algorithms succeed in hiding target itemsets while minimizing the side effects on the original database. What's more, they employ a projection mechanism to reduce the time spent scanning the database. Besides using the traditional evaluation criteria in PPDM, we also propose two additional similarity measures to evaluate the performance from the perspective of the itemsets and the structural integrity of the database. The experimental results indicate that the proposed algorithms can hide sensitive rare itemsets successfully and efficiently, and the evaluation methods used can become the evaluation criteria for privacy-preserving rare itemset mining (PPRIM).

Efficient Privacy-preserving Logistic Model With Malicious Security.

Conducting secure computations to protect against malicious adversaries is an emerging field of research. Current models designed for malicious security typically necessitate the involvement of two or more servers in an honest-majority setting. Among privacy-preserving data mining techniques, significant attention has been focused on the classification problem. Logistic regression emerges as a well-established classification model, renowned for its impressive performance. We introduce a novel matrix encryption method to build a maliciously secure logistic model. Our scheme involves only a single semi-honest server and is resilient to malicious data providers that may deviate arbitrarily from the scheme. The -transformation ensures that our scheme achieves indistinguishability (i.e., no adversary can determine, in polynomial time, which of the plaintexts corresponds to a given ciphertext in a chosen-plaintext attack). Malicious activities of data providers can be detected in the verification stage. A lossy compression method is implemented to minimize communication costs while preserving negligible degradation in accuracy. Experiments illustrate that our scheme is highly efficient to analyze large-scale datasets and achieves accuracy similar to non-private models. The proposed scheme outperforms other maliciously secure frameworks in terms of computation and communication costs.

Exploring Privacy-Preserving Methods via Perturbation Data Mining Employing Diverse Noise Strategies

Knowledge discovery from data, commonly referred to as data mining. it involves the extraction of significant information, which may be previously unknown, concealed, or relevant, from extensive data sets or databases through the utilization of statistical methodologies. With the introduction of enhanced hardware technologies, there has been a proliferation in the storage and recording of personal data pertaining to individuals. Sophisticated organizations employ data mining algorithms to uncover hidden patterns or insights within data. Data mining techniques find application in diverse fields such as marketing, medical diagnosis, forecasting system, and national security. However, in scenarios where data privacy is paramount, mining certain types of data without violating the privacy of data owners presents a formidable challenge, sparking growing concerns among privacy advocates. To address these concerns, it is imperative to advance data mining procedures that are complex to individual privacy considerations. Perturbation of data plays a pivotal role in Privacy-Preserving Data Mining (PPDM). Additive data safeguard data privacy. In contrast, multiplicative data perturbation involves a series of transformations, including rotation, translation, and the addition of noise components to the perturbed data copy.

Performance analysis of perturbation-based privacy preserving techniques: an experimental perspective

<span lang="EN-US">Nowadays, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several hybrid perturbation strategies that may be used to protect data privacy. For this, two perturbation-based techniques named improved random projection perturbation (IRPP) and enhanced principal component analysis-based technique (EPCAT) were used. These methods are employed to assess the precision, run time, and accuracy of the experimental results. This paper provides the impacts of perturbation-based privacy preserving techniques. It is observed that hybrid approaches are more efficient than the traditional approach.</span>

Effectiveness and Limitations of existing techniques for privacy preservation data mining (PPDM) for Medical Data

Effectiveness and Limitations of existing techniques for privacy preservation data mining (PPDM) for Medical Data

An unsupervised embedding harmonization system for privacy-preserving data mining in healthcare

Sharing data across hospitals for disease modeling is challenging due to concerns over patient privacy and the lack of an efficient privacy-preserving data mining framework. Contextual embedding models, which encode medical events into vector representations while preserving the contextual dependencies between events, have shown promise in privacy-preserving data mining without requiring original data disclosure. However, the medical event representations learned from multiple data sources lie in different embedding spaces and cannot be directly integrated. Existing embedding harmonization algorithms require a list of common medical events between different data sources and use them as corresponding pairs for transformation, known as the supervised harmonization method. However, common medical events can be difficult to collect in clinical practice. To promote data mining across hospitals, we developed a novel unsupervised embedding harmonization system that introduces an unsupervised harmonization algorithm to align contextual embeddings without the need for corresponding pairs. The proposed framework also considered different contextual embedding techniques, including Word2Vec and Med2Vec, to explore the robustness of the proposed unsupervised harmonization algorithm. The proposed framework was evaluated using medical events extracted from the Medical Information Mart for Intensive Care III database. By integrating the embeddings from multiple sources, the proposed framework can achieve better disease prediction accuracy and medical event clustering compared to models built on a single data source. The proposed unsupervised harmonization method, which achieves similar performance to the supervised harmonization model under different contextual embedding techniques, holds great promise for predictive modeling and event clustering in healthcare.

An edge-aided parallel evolutionary privacy-preserving algorithm for Internet of Things

Data sanitization in the context of Internet of Things (IoT) privacy refers to the process of permanently and irreversibly hiding all sensitive information from vast amounts of streaming data. Taking into account the dynamic and real-time characteristics of streaming IoT data, we propose a parallel evolutionary Privacy-Preserving Data Mining (PPDM), called High-performance Evolutionary Data Sanitization for IoT (HEDS4IoT), and implement two mechanisms on a Graphics Processing Units (GPU)-aided parallelized platform to achieve real-time streaming protected data transmission. The first mechanism, the Parallel Indexing Engine (PIE), generates retrieval index lists from the dataset using GPU blocks. These lists are used in place of the dataset during the PPDM process. The second mechanism, called Parallel Fitness Function Engine (PF2E), parallelizes the index lists on the GPU threads to speed up the computation of the quality of solutions generated by the evolutionary algorithm, in which deferential evolution is adopted as the evolutionary algorithm. In addition to the ability for Big data, the HEDS4IoT can be adaptively adjusted for dynamic nature of IoT where new streaming data is considered for data sanitization. Our experimental results with extensive benchmarks show that, at the kernel level, the PIE and PF2E mechanisms are averagely 33.5x and 53.7x faster than their CPU-implemented version, respectively. At the application level, our findings demonstrate that the HEDS4IoT can perform the PPDM process 47.7x faster than some of the state-of-art methods.

Hiding Sensitive Medical Data Using Simple and Pre-Large Rain Optimization Algorithm through Data Removal for E-Health System

Privacy has become a significant factor of e-Health system in the area of data mining termed as Privacy preserving data mining (PPDM) as it can uncover underlying rules and hide sensitive data for data sanitization. Various algorithms and heuristics have been studied to hide sensitive data using transaction removal. However, they are facing challenges to attain the reasonable side effects. Thus, rain optimization algorithm (ROA) based sensitive data hiding techniques is proposed in this paper. Using this algorithm, suitable transactions to be removed are selected. Besides, in this work, ROA based two frameworks are designed for data sanitization that are simple ROA to remove transaction (sROA2RT) and pre-large ROA to remove transaction (pROA2RT). In this algorithm, fitness is evaluated based on four side effects such as hiding failure, artificial cost, missing cost and dissimilarity of database. The proposed frameworks are evaluated using three e-Health datasets. Compared to previous frameworks, the proposed frameworks attain reasonable side effects.

STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation.

Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual's private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution. This work addresses the problem of PPDM by proposing statistical transformation with intuitionistic fuzzy (STIF) algorithm for data perturbation. The STIF algorithm contains statistical methods weight of evidence, information value and intuitionistic fuzzy Gaussian membership function. The STIF algorithm is applied on three benchmark datasets adult income, bank marketing and lung cancer. The classifier models decision tree, random forest, extreme gradient boost and support vector machines are used for accuracy and performance analysis. The results show that the STIF algorithm achieves 99% of accuracy for adult income dataset and 100% accuracy for both bank marketing and lung cancer datasets. Further, the results highlights that the STIF algorithm outperforms in data perturbation capacity and privacy preserving capacity than the state-of-art algorithms without any information loss on both numerical and categorical data.

Forecast of seasonal consumption behavior of consumers and privacy-preserving data mining with new S-Apriori algorithm

Forecast of seasonal consumption behavior of consumers and privacy-preserving data mining with new S-Apriori algorithm

Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

This study investigates existing input privacy-preserving data mining (PPDM) methods and privacy-preserving data stream mining methods (PPDSM), including their strengths and weaknesses. A further analysis was carried out to determine to what extent existing PPDM/PPDSM methods address the trade-off between data mining accuracy and data privacy which is a significant concern in the area. The systematic literature review was conducted using data extracted from 104 primary studies from 5 reputed databases. The scope of the study was defined using three research questions and adequate inclusion and exclusion criteria. According to the results of our study, we divided existing PPDM methods into four categories: perturbation, non-perturbation, secure multi-party computation, and combinations of PPDM methods. These methods have different strengths and weaknesses concerning the accuracy, privacy, time consumption, and more. Data stream mining must face additional challenges such as high volume, high speed, and computational complexity. The techniques proposed for PPDSM are less in number than the PPDM. We categorized PPDSM techniques into three categories (perturbation, non-perturbation, and other). Most PPDM methods can be applied to classification, followed by clustering and association rule mining. It was observed that numerous studies have identified and discussed the accuracy-privacy trade-off. However, there is a lack of studies providing solutions to the issue, especially in PPDSM.

On the Inverse Frequent Itemset Mining Problem for Condensed Representations of Itemsets

Inverse frequent itemset mining can be successfully modelled as an instance of the Probabilistic Satisfiability problem. Given a transaction database we can perform a frequent itemset mining algorithm, like the Apriori algorithm, to obtain useful itemset collections such as frequent or closed itemsets. We then use these itemset collections as frequency constraints in order to reconstruct the original database by solving a linear programming problem. There are cases however, where the reconstructed database is not in direct agreement with the original one. In this study, we analyse the degree of similarity between the original database and the reconstructed one, when different variations of condensed itemset representations are used as the initial frequency constraints. We examine how much the initial database properties and itemset relations have been preserved when we emphasize on the frequent itemset border and assess database quality by measuring database distance metrics. As this solution framework presents increased computational cost, we also consider a heuristic approach that is based on the notion that a transaction can be also considered as an itemset and compare the strengths and weaknesses of each framework when the same conditions apply. We manage to improve the efficiency of existing heuristic based approaches to the problem by utilizing smaller initial itemset collections. Our work contributes in (a) privacy preserving data mining and (b) gain in transaction database storage memory savings.

Privacy-Preserving Data Mining Techniques in Big Data: Balancing Security and Usability

The exponential growth of big data across industries presents both opportunities and challenges, particularly regarding the protection of sensitive information while maintaining data utility. The problem lies in balancing privacy preservation with the ability to extract meaningful insights from large datasets, which are often vulnerable to re-identification, breaches, and misuse. Current privacy-preserving data mining (PPDM) techniques, such as anonymization, differential privacy, and cryptographic methods, provide important solutions but introduce trade-offs in terms of data utility, computational performance, and compliance with privacy regulations. The objective of this study is to evaluate these PPDM methods, focusing on their effectiveness in safeguarding privacy while minimizing the impact on data accuracy and system performance. Additionally, the study seeks to assess the compliance of these methods with legal frameworks such as GDPR and HIPAA, which impose strict data protection requirements. By conducting an exhaustive analysis with regard to privacy-utility trade-offs, computation times, and communication complexities, this work attempts to outline the respective strengths and weaknesses of each method. Since these results can be elicited from the fact that indeed anonymization techniques contribute more to data utility by reducing the risk of re-identification, whereas differential privacy guarantees a high privacy at the cost of accuracy due to the introduction of noise in data through a privacy budget epsilon. Other cryptographic techniques, like homomorphic encryption and secure multiparty computation, are computationally expensive and hard to scale but offer strong security. In that respect, this work concludes that these techniques protect privacy with great efficiency; however, a number of privacy-data usability and performance trade-offs need to be performed. Future research should be focused on enhancing the scalability and efficiency of these methods toward fulfilling the needs of real-time big data analytics applications without loss of privacy.

Privacy-Preserving Data Mining and Analytics in Big Data

Privacy concerns have gotten more attention as Big Data has spread. The difficulties of striking a balance between the value of data and individual privacy have led to the emergence of privacy-preserving data mining and analytics approaches as a crucial area of research. An overview of the major ideas, methods, and developments in privacy-preserving data mining and analytics in the context of Big Data is given in this abstract. Data mining that protects privacy tries to glean useful insights from huge databases while shielding the private data of individuals. Commonly used in traditional data mining methods, sharing or pooling data might have serious privacy implications. On the other hand, privacy-preserving data mining strategies concentrate on creating procedures and algorithms that enable analysis without jeopardizing personal information. Finally, privacy-preserving data mining and analytics in the Big Data age bring important difficulties and opportunities. An overview of the main ideas, methods, and developments in privacy-preserving data mining and analytics are given in this abstract. It underscores the value of privacy in the era of data-driven decision-making and the requirement for effective privacy-preserving solutions to safeguard sensitive personal data while facilitating insightful analysis of huge datasets.

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts. It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle. In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’ privacy. The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them. This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm. Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets. Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.

STUDY OF BIG DATA BASED PROBLEMS FOR DATA ANONYMIZATION

In order to defeat typical attacks like the similarity attack, the probabilistic inference attack, and others that are possible with anonymized data, we have studied different techniques. To anonymize the data set and disseminate the anonymized data set on a distributed environment without endangering data privacy, a privacy-preserving distributed framework is suggested in most of the techniques. It is possible to achieve a better balance between privacy and data utility, and the data utility is demonstrated in terms of conventional measures. The privacy-preserved data set is also subjected to the application of several classifiers in order to measure the utility of the data When sharing and processing data in a distributed setting or with the Internet of Things, data privacy is a crucial requirement. High communication and computational costs are involved in collaborative privacy-preserving data mining based on secured multiparty computation. Data protection against identity revelation is achieved by the use of data anonymization, a promising technology in the field of privacy-preserving data mining. Anonymization faces significant difficulties, including information loss and frequent attacks that may be made on the anonymized data. Utilizing data mining techniques, data anonymization has recently demonstrated a considerable increase in data value. Still, the methods now in use are ineffective for dealing with attacks. Therefore, a clustering-based anonymization approach that is resistant to similarity attacks and attacks based on inference is suggested in this study. On the Hadoop Distributed File System, the anonymized data is dispersed. The technique creates a better balance between utility and privacy.