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Article
Publication date: 24 August 2021

K. Sujatha and V. Udayarani

The purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information…

Abstract

Purpose

The purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback.

Design/methodology/approach

In this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.

Findings

Analysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated.

Originality/value

The paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 10 October 2022

Nidhi Sharma and Ravindara Bhatt

Privacy preservation is a significant concern in Internet of Things (IoT)-enabled event-driven wireless sensor networks (WSNs). Low energy utilization in the event-driven system…

Abstract

Purpose

Privacy preservation is a significant concern in Internet of Things (IoT)-enabled event-driven wireless sensor networks (WSNs). Low energy utilization in the event-driven system is essential if events do not happen. When events occur, IoT-enabled sensor network is required to deal with enormous traffic from the concentration of demand data delivery. This paper aims to explore an effective framework for safeguarding privacy at source in event-driven WSNs.

Design/methodology/approach

This paper discusses three algorithms in IoT-enabled event-driven WSNs: source location privacy for event detection (SLP_ED), chessboard alteration pattern (SLP_ED_CBA) and grid-based source location privacy (GB_SLP). Performance evaluation is done using simulation results and security analysis of the proposed scheme.

Findings

The sensors observe bound events or sensitive items within the network area in the field of interest. The open wireless channel lets an opponent search traffic designs, trace back and reach the start node or the event-detecting node. SLP_ED and SLP_ED_CBA provide better safety level results than dynamic shortest path scheme and energy-efficient source location privacy protection schemes. This paper discusses security analysis for the GB_SLP. Comparative analysis shows that the proposed scheme is more efficient on safety level than existing techniques.

Originality/value

The authors develop the privacy protection scheme in IoT-enabled event-driven WSNs. There are two categories of occurrences: nominal events and critical events. The choice of the route from source to sink relies on the two types of events: nominal or critical; the privacy level required for an event; and the energy consumption needed for the event. In addition, phantom node selection scheme is designed for source location privacy.

Details

International Journal of Pervasive Computing and Communications, vol. 19 no. 5
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 4 May 2022

Dhanya Pramod

This study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the…

Abstract

Purpose

This study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the extent of adopting privacy-preserving RSs and postulates the future direction of research in RS security.

Design/methodology/approach

The study gathered articles from well-known databases such as SCOPUS, Web of Science and Google scholar. A systematic literature review using PRISMA was carried out on the 41 papers that are shortlisted for study. Two research questions were framed to carry out the review.

Findings

It is evident from this study that privacy issues in the RS have been addressed with various techniques. However, many more challenges are expected while leveraging technology advancements for fine-tuning recommenders, and a research agenda has been devised by postulating future directions.

Originality/value

The study unveils a new comprehensive perspective regarding privacy preservation in recommenders. There is no promising study found that gathers techniques used for privacy protection. The study summarizes the research agenda, and it will be a good reference article for those who develop privacy-preserving RSs.

Details

Data Technologies and Applications, vol. 57 no. 1
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 17 August 2012

Kun Guo and Qishan Zhang

The purpose of this paper is to provide a privacy preserving method based on grey model and apply it to clustering, so that the privacy of the individuals can be protected while…

145

Abstract

Purpose

The purpose of this paper is to provide a privacy preserving method based on grey model and apply it to clustering, so that the privacy of the individuals can be protected while the information loss is kept low.

Design/methodology/approach

GM(1,1) model is utilized reversely in the approach to add noise to the original data, so as to make use of the grey information to blur the true one.

Findings

It is shown that the privacy preserving method based on grey model can achieve both high effectiveness and high efficiency.

Originality/value

The paper presents the first attempt to apply the grey model to protect data privacy. The experimental results show the effectiveness and the efficiency of the proposed method.

Details

Grey Systems: Theory and Application, vol. 2 no. 2
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 13 March 2017

Nikolaos Polatidis, Christos K. Georgiadis, Elias Pimenidis and Emmanouil Stiakakis

This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender…

Abstract

Purpose

This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable.

Design/methodology/approach

This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware data set has been used to run performance and usability tests.

Findings

The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected.

Originality/value

This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used.

Details

Information & Computer Security, vol. 25 no. 1
Type: Research Article
ISSN: 2056-4961

Keywords

Article
Publication date: 20 March 2017

Mortaza S. Bargh, Sunil Choenni and Ronald Meijer

Information dissemination has become a means of transparency for governments to enable the visions of e-government and smart government, and eventually gain, among others, the…

Abstract

Purpose

Information dissemination has become a means of transparency for governments to enable the visions of e-government and smart government, and eventually gain, among others, the trust of various stakeholders such as citizens and enterprises. Information dissemination, on the other hand, may increase the chance of privacy breaches, which can undermine those stakeholders’ trust and thus the objectives of transparency. Moreover, fear of potential privacy breaches compels information disseminators to share minimum or no information. The purpose of this study is to address these contending issues of information disseminations, i.e. privacy versus transparency, when disseminating judicial information to gain (public) trust. Specifically, the main research questions are: What is the nature of the aforementioned “privacy–transparency” problem and how can we approach and address this class of problems?

Design/methodology/approach

To address these questions, the authors have carried out an explorative case study by reconsidering and analyzing a number of information dissemination cases within their research center for the past 10 years, reflecting upon the whole design research process, consulting peers through publishing a preliminary version of this contribution and embedding the work in an in-depth literature study on research methodologies, wicked problems and e-government topics.

Findings

The authors show that preserving privacy while disseminating information for transparency purposes is a typical wicked problem, propose an innovative designerly model called transitional action design research (TADR) to address the class of such wicked problems and describe three artifacts which are designed, intervened and evaluated according to the TADR model in a judicial research organization.

Originality/value

Classifying the privacy transparency problem in the judicial settings as wicked is new, the proposed designerly model is innovative and the realized artifacts are deployed and still operational in a real setting.

Details

Transforming Government: People, Process and Policy, vol. 11 no. 1
Type: Research Article
ISSN: 1750-6166

Keywords

Article
Publication date: 11 September 2023

Balakrishnan Unny R., Samik Shome, Amit Shankar and Saroj Kumar Pani

This study aims to provide a systematic review of consumer privacy literature in the context of smartphones and undertake a comprehensive analysis of academic research on this…

Abstract

Purpose

This study aims to provide a systematic review of consumer privacy literature in the context of smartphones and undertake a comprehensive analysis of academic research on this evolving research area.

Design/methodology/approach

This review synthesises antecedents, consequences and mediators reported in consumer privacy literature and presents these factors in a conceptual framework to demonstrate the consumer privacy phenomenon.

Findings

Based on the synthesis of constructs reported in the existing literature, a conceptual framework is proposed highlighting antecedents, mediators and outcomes of experiential marketing efforts. Finally, this study deciphers overlooked areas of consumer privacy in the context of smartphone research and provides insightful directions to advance research in this domain in terms of theory development, context, characteristics and methodology.

Originality/value

This study significantly contributes to consumer behaviour literature, specifically consumer privacy literature.

Details

Journal of Consumer Marketing, vol. 41 no. 1
Type: Research Article
ISSN: 0736-3761

Keywords

Article
Publication date: 17 June 2021

Ankush Balaram Pawar, Shashikant U. Ghumbre and Rashmi M. Jogdand

Cloud computing plays a significant role in the initialization of secure communication between users. The advanced technology directs to offer several services, such as platform…

Abstract

Purpose

Cloud computing plays a significant role in the initialization of secure communication between users. The advanced technology directs to offer several services, such as platform, resources, and accessing the network. Furthermore, cloud computing is a broader technology of communication convergence. In cloud computing architecture, data security and authentication are the main significant concerns.

Design/methodology/approach

The purpose of this study is to design and develop authentication and data security model in cloud computing. This method includes six various units, such as cloud server, data owner, cloud user, inspection authority, attribute authority, and central certified authority. The developed privacy preservation method includes several stages, namely setup phase, key generation phase, authentication phase and data sharing phase. Initially, the setup phase is performed through the owner, where the input is security attributes, whereas the system master key and the public parameter are produced in the key generation stage. After that, the authentication process is performed to identify the security controls of the information system. Finally, the data is decrypted in the data sharing phase for sharing data and for achieving data privacy for confidential data. Additionally, dynamic splicing is utilized, and the security functions, such as hashing, Elliptic Curve Cryptography (ECC), Data Encryption Standard-3 (3DES), interpolation, polynomial kernel, and XOR are employed for providing security to sensitive data.

Findings

The effectiveness of the developed privacy preservation method is estimated based on other approaches and displayed efficient outcomes with better privacy factor and detection rate of 0.83 and 0.65, and time is highly reduced by 2815ms using the Cleveland dataset.

Originality/value

This paper presents the privacy preservation technique for initiating authenticated encrypted access in clouds, which is designed for mutual authentication of requester and data owner in the system.

Details

International Journal of Pervasive Computing and Communications, vol. 19 no. 2
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 19 May 2020

Praveen Kumar Gopagoni and Mohan Rao S K

Association rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation…

Abstract

Purpose

Association rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.

Design/methodology/approach

The primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.

Findings

The experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.

Originality/value

Data mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

Details

Data Technologies and Applications, vol. 54 no. 3
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 3 November 2022

Yaqi Liu, Shuzhen Fang, Lingyu Wang, Chong Huan and Ruixue Wang

In recent years, personalized recommendations have facilitated easy access to users' personal information and historical interactions, thereby improving recommendation…

Abstract

Purpose

In recent years, personalized recommendations have facilitated easy access to users' personal information and historical interactions, thereby improving recommendation effectiveness. However, due to privacy risk concerns, it is essential to balance the accuracy of personalized recommendations with privacy protection. Accordingly, this paper aims to propose a neural graph collaborative filtering personalized recommendation framework based on federated transfer learning (FTL-NGCF), which achieves high-quality personalized recommendations with privacy protection.

Design/methodology/approach

FTL-NGCF uses a third-party server to coordinate local users to train the graph neural networks (GNN) model. Each user client integrates user–item interactions into the embedding and uploads the model parameters to a server. To prevent attacks during communication and thus promote privacy preservation, the authors introduce homomorphic encryption to ensure secure model aggregation between clients and the server.

Findings

Experiments on three real data sets (Gowalla, Yelp2018, Amazon-Book) show that FTL-NGCF improves the recommendation performance in terms of recall and NDCG, based on the increased consideration of privacy protection relative to original federated learning methods.

Originality/value

To the best of the authors’ knowledge, no previous research has considered federated transfer learning framework for GNN-based recommendation. It can be extended to other recommended applications while maintaining privacy protection.

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