Privacy Preserving Association Rule Mining Research Articles

Privacy-preserving association rule mining: a survey of techniques for sensitive rule identification and enhanced data protection

Over the past few decades, pervasive computing has generated increasing amounts of data. Data mining (DM) technology is becoming more widely used. Due to the potential threats to the confidentiality of people and organizations, the massive collecting and analysis of data might raise privacy issues. A new research area termed privacy-preserving association rule mining (PPARM), which has attracted the interest of many researchers, has emerged to prevent privacy disclosure during DM. The research field PPARM, aims to prevent association rule mining privacy infractions from happening. The study uses a systematic survey to understand the literature on PPARM techniques for sensitive rule identification and enhanced data protection. Here, the application area of PPARM has been explored, summarized, and concluded with some open challenges and future directions.

Privacy-preserving association rule mining via multi-key fully homomorphic encryption

Association rule mining is an efficient method to mine the association relationships between different items from large transaction databases, but is vulnerable to privacy leakage as operates over users’ sensitive data directly. Privacy-preserving association rule mining has emerged to protect users’ privacy during rule mining. Unfortunately, existing privacy-preserving association rule mining schemes suffer from high overhead, fail to support multiple users, and are challenging to prevent collusion attacks between twin-server. To this end, in this paper, we propose a privacy-preserving association rule mining solution via multi-key fully homomorphic encryption over the torus (MKTFHE), which efficiently supports multiple users through a single server only. Specifically, we first construct some multi-key homomorphic gates based on MKTFHE. Then, we designed a series of privacy-preserving computational protocols based on multi-key homomorphic gates. Finally, we build a privacy-preserving association rule mining system with a single cloud server to support multiple users. Moreover, privacy analysis and performance evaluation demonstrate our proposal is efficient and feasible. In contrast to existing solutions, the proposed scheme outperforms encryption and communication, saving approximately 8.5% running time.

A new method for privacy preserving association rule mining using homomorphic encryption with a secure communication protocol

A new method for privacy preserving association rule mining using homomorphic encryption with a secure communication protocol

DESIGN AND DEVELOPMENT OF ANTI-LOCK BRAKING SYSTEM FOR ELECTRIC VEHICLE

Abstract—Recently, privacy-preserving association rules mining algorithms have been proposed to support data privacy. However, the algorithms have an additional overhead to insert fake items (or fake transactions) and cannot hide data frequency. In this paper, we propose a privacy-preserving association rule mining algorithm for encrypted data in cloud computing. For association rule mining, we utilize Apriori algorithm by using the Elgamal cryptosystem, without additional fake transactions. Thus the proposed algorithm can guarantee both data privacy and query privacy, while concealing data frequency. We show that the proposed algorithm achieves about 3-5 times better performance than the existing algorithm, in terms of association rule mining time. Keywords- association rule mining; Apriori algorithm; encrypted data; cloud computing; Elgamal cryptosystem;

NOVEL PRIVACY PRESERVING ASSOCIATION RULE MINING FOR HORIZONTALLY PARTITIONED DATABASES

Advancement in scientific research depends on the availability and sharing of information and ideas. Many authors proposed many methodologies for isolation preserving data mining for centralized as well as for distributed database. The core intend of this paper work is, to develop efficient methodologies to find privacy preserving association rule mining in centralized as well as in distributed database environment without violating any privacy constraints. The issue of privacy constraints for centralized database environment is entirely different from distributed database environment. So the approaches for privacy preserving association rule mining in centralized are also different from approaches in distributed environment. Achieving privacy preserving goals in both these environments in the process of mining is a challenging task.

Verifiable privacy-preserving association rule mining using distributed decryption mechanism on the cloud

As one of the important ways in data mining, the association rule mining is to analyze the correlation of transactions based on massive data and mine the hidden valuable information. However, excessive data collection and analysis might lead to the privacy leakage of user data and the damage of data integrity. Meanwhile, in the existing privacy-preserving schemes, the cloud servers such as the evaluator have strong decryption capabilities, which causes active attacks easily, or data reliability in mining and analysis is not considered. In response to these problems, this paper proposes a verifiable privacy-preserving association rule mining scheme (VPPARM) using distributed decryption mechanism on the cloud. First, the scheme adopts distributed decryption to complete the data mining tasks through the dual-cloud servers, which weakens the decryption ability of the cloud servers and prevents the server from active attacks. Secondly, to protect the privacy of the association rule mining process, our scheme adopts adding virtual transactions, permutations, and random number masking collaboratively to hide the data in the whole mining process. In addition, to eliminate the hidden danger of illegal users, our scheme adopts a verifiable short digital signature scheme to verify data integrity and ensure data reliability, avoiding the poisoning attack. Finally, through the performance evaluation, the results demonstrate that our scheme realizes correctness, security, reliability with lower communication and computation costs and improves efficiency to a certain extent.

ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data

In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.

Privacy-Preserving Association Rule Mining Considering Multi-objective through an Evolutionary Algorithm

Privacy-Preserving Association Rule Mining Considering Multi-objective through an Evolutionary Algorithm

Privacy-preserving association rule mining based on electronic medical system

Privacy protection during collaborative distributed association rule mining is an important research, which has been widely used in market prediction, medical research and other fields. In medical research, Domadiya et al. (Sadhana 43(8):127, 2018) focused on mining association rules from horizontally distributed healthcare data to diagnose heart disease. They claimed they proposed a more effective privacy-preserving distributed association rule mining (PPDARM) scheme. However, a serious security scrutiny of the scheme is performed, and we find it vulnerable to protect the support of the itemsets from any electronic health record (EHR) system, which is the most important parameter Domadiya et al. tried to protect. In this paper, we first present the cryptanalysis of the PPDARM scheme proposed by Domadiya et al. as well as some revised performance analyses. Then a new PPDARM scheme with less interactions is proposed to avert the shortcomings of Domadiya et al., using the homomorphic properties of the distributed Paillier cryptosystem to accomplish the cooperative computation. Our scheme allows the directed authority (miner) to obtain the final results rather than all cooperative EHR systems, in case of semi-honest but pseudo EHR systems. Moreover, security analysis and performance evaluation demonstrate our proposal is efficient and feasible.

Privacy Preserving Association Rule Mining on Distributed Healthcare Data: COVID-19 and Breast Cancer Case Study.

Association rule mining can be used in healthcare data mining to provide solutions to life-threatening diseases like recent COVID-19. Due to healthcare data privacy concerns, privacy preserving distributed healthcare data mining becomes the primary focus of medical science research. Recently, Chahar et al. (Sādhanā 42:1997–2007, 2017) proposed privacy preserving distributed association rule mining scheme with insecure communication channels. They used the concept of an elliptic curve-based paillier cryptosystem to achieve privacy, authenticity, and integrity. We observed some security vulnerabilities in their privacy preserving association rule mining scheme when implemented with insecure communication channels. We observed that the security vulnerabilities will result in the disclosure of private data of sites (or participants). Furthermore, we propose a secure version of their scheme to solve the security vulnerabilities with insecure communication channels. Theoretical and experimental analysis shows that the proposed scheme has almost equal computation and communication complexities with better securities. A case study on the effectiveness of the proposed approach in combating COVID-19 coronavirus and Breast Cancer is also discussed.

High-performance implementation of evolutionary privacy-preserving algorithm for big data using GPU platform

Privacy protection has become a predominant concern in big data analysis. Privacy-Preserving Association Rule Mining (PPARM) is a process in which original data is transformed into a sanitized version to remove sensitive patterns. Although evolutionary PPARM approaches were developed, they are inefficient because the fitness function is overwhelmingly expensive. In this paper, an efficient algorithm, GPU-based Evolutionary Privacy-Preserving (GEPP), for improving the performance of PPARM is presented. We introduce two strategies for this reason: the first one is to develop a parallel indexing machine to generate retrieval index lists by parallelizing dataset scanning between GPU blocks, and the second strategy is the parallelization of the index lists for fitness function computation using CPU/GPU platform. In the second strategy, search mechanism and fitness function steps are performed on CPU and GPU, respectively. The scalability of GEPP is evaluated in terms of GPU characteristics, e.g., blocks and threads, data characteristics, e.g., the number of transactions, items, and density ratio, and patterns characteristics, e.g., the ratio of sensitive patterns and support thresholds. Experimental results show that our parallel implementation of the proposed approach obtains a speedup up to 36.6x, on average, compared to the CPU implementation using real and synthetic large-scale transaction datasets.

Secure Privacy-Preserving Association Rule Mining With Single Cloud Server

To preserve the privacy of data uploaded on the cloud, it is widely accepted to encrypt the data before uploading it. This leads to the challenge of data analysis, especially association rule mining while protecting data privacy. As one of the solutions, homomorphic encryption is presented allowing encrypted data processing without decryption. In particular, the twin-cloud structure is frequently applied in the privacy-preserving association rule mining schemes based on asymmetric homomorphic encryption, which contradicts the reality that most of the practical applications applied the single cloud server. However, the existing related single cloud server schemes suffer from privacy leakage problems. To fill this gap in the literature, in this paper, we first present a universal secure multiplication protocol with the single cloud server using the garbled circuit and additive homomorphic encryption. Based on this multiplication protocol, we construct the inner product protocol, comparison protocol, frequent itemset protocol, and the final association rule mining protocol that is secure against privacy leakage. Finally, we give the theoretical security analysis of the proposed protocols and show its performance analysis.

클라우드 컴퓨팅 환경에서 개인정보를 보호하는 FP-Growth 기반 연관 규칙 마이닝 알고리즘

클라우드 컴퓨팅 환경에서 개인정보를 보호하는 FP-Growth 기반 연관 규칙 마이닝 알고리즘

Privacy-Preserving Association Rule Mining Using Homomorphic Encryption in a Multikey Environment

With the development of big data and cloud computing, data analysis technologies play an important role to produce huge market values. Customers with limited computing resources may resort to the cloud to perform some association rule mining tasks. Data owners may have a risk of personal sensitive information leakage in this process. To preserve privacy in outsourced data, data owners may encrypt raw data before uploading. Data analysis of encrypted data is a challenge that has attracted the attention of many researchers in recent years. Homomorphic encryption is a cryptographic tool, which is one of the ways to solve this challenge. It allows data processing of encrypted data without decryption. Researching homomorphic encryption schemes that support privacy-preserving data mining in a multikey environment has become a significant direction. In this article, we propose a novel homomorphic cryptosystem, which supports multiple cloud users to have different public keys. Besides, we propose a privacy-preserving association rule mining scheme on outsourced data uploaded from multiple parties in a twin-cloud architecture. Our scheme uses a transaction record representation method in databases for large shopping malls based on real-world situations, and our experiments on a real transaction database show that our technology is reasonably feasible.

Privacy Preserving with Association Rule Mining using Evolutionary Algorithm

Privacy-Preserving-Data-Mining (PPDM) is a novel study which goals to protect the secretive evidence also circumvent the revelation of the evidence through the records reproducing progression. This paper focused on the privacy preserving on vertical separated databases. The designed methodology for the subcontracted databases allows multiple data viewers besides vendors proficiently to their records securely without conceding the secrecy of the data. Privacy Preserving Association Rule-Mining (PPARM) is one method, which objects to pelt sensitivity of the association imperative. A new efficient approach lives the benefit since the strange optimizations algorithms for the delicate association rule hiding. It is required to get leak less information of the raw data. The evaluation of the efficient of the proposed method can be conducting on some experiments on different databases. Based on the above optimization algorithm, the modified algorithm is to optimize the association rules on vertically and horizontally separated database and studied their performance.

Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony

Association Rule Hiding (ARH) is the process of protecting sensitive knowledge using data transformation. Although there are some evolutionary-based ARH algorithms, they mostly focus on the itemset hiding instead of the rule hiding. Besides, unstable convergence to the global optimum solution and designing long solutions make them inappropriate in reducing side effects. They use the basic versions of evolutionary approaches, resulting in inappropriate performance in ARH domain where the search space is large and the algorithms easily get trapped in local optima. To deal with these problems, we propose a new rule hiding algorithm based on a binary Artificial Bee Colony (ABC) approach which has good exploration. However, we improve the binary ABC algorithm to enhance its poor exploitation by designing a new neighborhood generation mechanism to balance between exploration and exploitation. We called this algorithm Improved Binary ABC (IBABC). IBABC approach is coupled with our proposed rule hiding algorithm, called ABC4ARH, to select sensitive transactions for modification. To choose victim items, ABC4ARH formulates a heuristic. The performance of ABC4ARH algorithm on the side effects is demonstrated using extensive experiments conducted on five real datasets. Furthermore, the effectiveness of IBABC is verified using the uncapacitated facility location problem and 0–1 knapsack problem.

Protecting the Privacy of Cancer Patients Using Fuzzy Association Rule Hiding

Objective:Privacy protection in the medical field means the protection of individuals from being associated with undesirable conditions, diagnoses or treatments (Sensitive Attributes). The problem of knowledge discovery from health care data by applying data mining algorithms is inversely related to the privacy of individuals. Due to the tremendous growth of data in a large scale, there is a demand to protect the sensitive data accessible from medical datasets.Methods:This paper considers the problem of building privacy preserving association rule mining algorithm using the notion of TF * IDF derived from the information retrieval domain. The highly sensitive transaction is chosen using the product of Relative Item Frequency and Condensed Frequency. Finally, sensitive fuzzy data is perturbed to hide these refined rules.Results:It has been found that the number of non-sensitive rules lost as a side effect of hiding sensitive rule is 20% less and number of ghost rules is 30% less in proposed work than in previous work using Transactional Impact factor method. The execution time of hiding a rule is 26% lesser on an average in the proposed technique for various values of minimum confidence threshold. It has been observed that the number of modifications to the original dataset after hiding three rules were reduced by 66% in proposed method than in previous work. As the number of modifications to original data is less the chances of generating false association is also reduced.Conclusion:In this paper, a novel method was presented to hide the sensitive rule in quantitative data by decreasing the support of the RHS of the rule. Experimental results demonstrate that the proposed approach is more efficient as it facilitates better rule hiding and minimizes the number of lost rules and ghost rules. Also, this approach makes minimum modifications to the dataset.

Privacy Preserving Association Rule Mining: Taxonomy, Techniques, and Metrics

In the last decades, pervasive computing is generating a growing quantity of data. Data mining (DM) technology has become increasingly popular. However, the excessive collection and analysis of data may violate the privacy of individuals and organizations, which raises privacy concern. Therefore, a new research area known as privacy-preserving DM (PPDM) has emerged and attracted the attention of many researchers who are interested in preventing privacy disclosure during DM. In this paper, we provide a comprehensive review of studies on a specific PPDM, known as privacy-preserving association rule mining (PPARM). We present a detailed taxonomy for the existing PPARM algorithms according to multiple dimensions and then conduct a survey of the most relevant PPARM techniques from the literature. Moreover, we survey and elaborate on each type of metrics used to evaluate PPARM algorithms. Finally, we summarize some conclusions and come up with some future directions and challenges.

Privacy Preserving Distributed Association Rule Mining Approach on Vertically Partitioned Healthcare Data

The trends of data mining in the healthcare is increased due to the digitization of healthcare with electronic health record (EHR) systems. This generates a huge amount of data on daily basis. Data mining with the healthcare data has given the new direction to medical research for early detection of diseases and improving patient care. Many data mining applications require the integration of data from the different sources. For example, the integration of outpatient medical records and health examination data helps to identify the correlation between abnormal test result and disease. The result of association rule mining on this integrated data helps to build the knowledge center for disease prevention, which facilitate the healthcare provider in follow up treatment and prevention. The integration of data requires the sharing of sensitive information about the patients. Disclosing the sensitive information violates the privacy of patients. In this paper, we tackle the problem of privacy preserving association rule mining in vertically partition healthcare data. Furthermore, we analyze the proposed approach in terms of privacy preservation, communication and computation cost.

Privacy-preserving association rule mining for horizontally partitioned healthcare data: a case study on the heart diseases

In recent years, a trend of electronic health record (EHR) system can be seen increasingly in the hospitals, which has generated huge amount of electronically stored data of patients. Association rule mining technique is very helpful in the numerous applications of healthcare (e.g., correlation between disease and symptoms, disease and offering effective treatment and predicting risks of disease based on the historical data, etc.). The data collected by an EHR system are very important for the medical research. Currently, a patient health report is derived on the basis of a physician’s own experience and on the association rule mining results of a local EHR system maintained by a particular hospital. Association rule mining results will be more accurate if the data of all local EHR systems are integrated and association rule mining is performed. Integration of local EHR systems requires the sharing of local EHR data. Sharing of patient records violates the privacy of patients. Hence, medical research is focused on the problem of mining association rules without sharing of local private EHR data. Privacy-preserving distributed association rule mining (PPDARM) solves this issue by mining the association rules while preserving the privacy of patients. In this paper, an approach for the PPDARM is proposed for collaboratively performing association rule mining by all local EHR systems while preserving the privacy. The proposed approach is also analysed with the heart disease dataset.