Privacy-preserving Model Research Articles

A Privacy Preservation Strategy Using Hybrid Fully Homomorphic Encryption Scheme in IoT

The Wireless Sensor Network affords the basis for the Internet of Things (IoT) systems that typically produce large and imprecise information. This information has to be integrated faster and efficiently handled. Various devices deployed with IoT exists like organizational automation, smart grid etc. To sub-ordinate this system software as well as their applications, the Packet Authentication Code (PAC) and routing protocols are essentially required for interoperability and scalability. Hence, this study intends to propose a system based on PAC to preserve privacy, thereby handling the False Data Injection (FDI) attacks. The study performs the authentication process where the nodes are authenticated even before the data transmission for efficient security. A privacy preservation model is also employed to deal with the FDI attacks. Data aggregation is also performed to reduce the end-to-end delay and communication overhead. A hybrid Gentry, Sahai and Waters (GSW) and Ducas and Micciancio (DM) are proposed to handle the FDI attacks through a key generation process. In addition, a PAC is generated to improvise the security that includes various processes such as group formation of the node members, key generation, as well as distribution and unique sensor number generation to authenticate using PAC. The performance of the proposed system is analyzed through a comparative analysis in terms of specific significant parameters to evaluate the efficacy of the proposed method. The analytical results reveal the efficiency of the proposed hybrid GSW–DM with PAC for privacy preservation than the existing systems.

An Efficient Multi-Class Privacy-Preserving-Based Encryption Framework for Large Distributed Databases

This paper introduces a novel hybrid filter-based ensemble multi-class classification model for distributed privacy-preserving applications. The conventional privacy-preserving multi-class learning models have limited capacity to enhance the true positive rate, mainly due to computational time and memory constraints, as well as the static nature of metrics for parameter optimization and multi-class perturbation processes. In this research, we develop the proposed model on large medical and market databases with the aim of enhancing multi-party data confidentiality through a security framework during the privacy-preserving process. Moreover, we also introduce a secure multi-party data perturbation process to improve computational efficiency and privacy-preserving performance. Experimental results were evaluated on different real-time privacy-preserving datasets, such as medical and market datasets, using different statistical metrics. The evaluation results demonstrate that the proposed multi-party-based multi-class privacy-preserving model performs statistically better than conventional approaches.

A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets

The widespread application of multimedia technologies such as video surveillance, online meetings, and drones facilitates the acquisition of a large amount of data that may contain facial features, posing significant concerns with regard to privacy. Protecting privacy while preserving the semantic contents of facial images is a challenging but crucial problem. Contemporary techniques for protecting the privacy of images lack the incorporation of the semantic attributes of faces and disregard the protection of dataset privacy. In this paper, we propose the Facial Privacy and Semantic Preservation (FPSP) model that utilizes similar facial feature replacement to achieve identity concealment, while adding semantic evaluation to the loss function to preserve semantic features. The proposed model is versatile and efficient in different task scenarios, preserving image utility while concealing privacy. Our experiments on the CelebA dataset demonstrate that the model achieves a semantic preservation rate of 77% while concealing the identities in facial images in the dataset.

Privacy-Preserving Blockchain Framework for Supply Chain Management: Perceptive Craving Game Search Optimization (PCGSO)

The fierce competition in international markets and the rapid advancements in information technology result in shorter lead times, lower transportation capacity, and higher demand. The supply chain network is one of the most crucial areas of concentration in the majority of business circumstances. Blockchain technology is a promising option for safe information exchange in the supply chain network. Although preserving security at every level of the blockchain is somewhat important, cryptographic methodologies are frequently used in the existing works. The novel perceptive craving game search (PCGS) optimization algorithm is used to optimally generate the key for data sanitization, which assures the privacy of logistics data. Here, the original logistics data obtained from the manufacturer is sanitized with an optimal key generated by using the PCGS optimization algorithm, avoiding the risk of unauthorized access and data swarm that causes the system to lag. Moreover, the sanitized data obtained from the manufacturer is transmitted to the allowed parties via different sub-chains. The same generated key is used on the receiving customer side for reconstructing the original information from the sanitized data. The performance and results of the proposed blockchain-based privacy preservation model are validated using various parameters.

A Systematic Review of Privacy Preservation Models in Wireless Networks

A Systematic Review of Privacy Preservation Models in Wireless Networks

A smart privacy preserving framework for industrial IoT using hybrid meta-heuristic algorithm

Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and security when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper–Black Hole Optimization (G–BHO) algorithm. A multi-objective function involving the parameters like degree of modification, hiding rate, correlation coefficient between the actual data and restored data, and information preservation rate was derived and utilized for generating optimal key. The simulation result establishes the dominance of the proposed model over other state-of the-art models in terms of various performance metrics. In respect of privacy preservation, the proposed G–BHO algorithm has achieved 1%, 15.2%, 12.6%, and 1% enhanced result than JA, GWO, GOA, and BHO, respectively.

Improved Association Rule Mining-Based Data Sanitization for Privacy Preservation Model in Cloud

Data security in cloud services is achieved by imposing a broad range of privacy settings and restrictions. However, the different security techniques used fail to eliminate the hazard of serious data leakage, information loss and other vulnerabilities. Therefore, better security policy requirements are necessary to ensure acceptable data protection levels in the cloud. The two procedures presented in this paper are intended to build a new cloud data security method. Here, sensitive data stored in big datasets is protected from abuse via the data sanitization procedure relying on an improved apriori approach to clean the data. The main objective in this case is to generate a key using an optimization technique known as Corona-integrated Archimedes Optimization with Tent Map Estimation (CIAO-TME). Such a technique deals with both restoration and sanitization of data. The problem of optimizing the data preservation ratio (IPR), the hiding ratio (HR), and the degree of modification (DOM) is formulated and researched as well.

Role of Machine Learning on Key Extraction for Data Privacy Preservation of Health Care Sectors in IoT Environment

Abstract Privacy and security in the medical field are major aspects to consider in the current era. This is due to the huge necessity for data among providers, payers and patients, respectively. Recently, more researchers are making their contributions in this field under different aspects. But, there need more enhancements concerning security. In this circumstance, this paper intends to propose a new IoT-dependent health care privacy preservation model with the impact of the machine learning algorithm. Here, the information or data from IoT devices is subjected to the proposed sanitization process via generating the optimal key. In this work, the utility of the machine learning model is the greatest gateway to generating optimal keys as it is already trained with the neural network. Moreover, identifying the optimal key is the greatest crisis, which is done by a new Improved Dragonfly Algorithm algorithm. Thereby, the sanitization process works, and finally, the sanitized data are uploaded to IoT. The data restoration is the inverse process of sanitization, which gives the original data. Finally, the performance of the proposed work is validated over state-of-the-art models in terms of sanitization and restoration analysis.

Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective

The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance.

EPMLF: Efficient and Privacy-Preserving Machine Learning Framework Based on Fog Computing

With the continuous improvement of computation and communication capabilities, the Internet of Things (IoT) plays a vital role in many intelligent applications. Therefore, IoT devices generate a large amount of data every day, which lays a solid foundation for the success of machine learning. However, the strong privacy requirements of the IoT data make its machine learning very difficult. To protect data privacy, many privacy-preserving machine learning schemes have been proposed. At present, most schemes only aim at specific models and lack general solutions, which is not an ideal solution in engineering practice. In order to meet this challenge, we propose an efficient and privacy-preserving machine learning training framework (ePMLF) in a fog computing environment. The ePMLF framework can let the software service provider (SSP) perform privacy-preserving model training with the data on the fog nodes. The security of the data on the fog nodes can be protected and the model parameters can only be obtained by SSP. The proposed secure data normalization method in the framework further improves the accuracy of the training model. Experimental analysis shows that our framework significantly reduces the computation and communication overhead compared with the existing scheme.

Privacy-preserving cancer type prediction with homomorphic encryption

Cancer genomics tailors diagnosis and treatment based on an individual’s genetic information and is the crux of precision medicine. However, analysis and maintenance of high volume of genetic mutation data to build a machine learning (ML) model to predict the cancer type is a computationally expensive task and is often outsourced to powerful cloud servers, raising critical privacy concerns for patients’ data. Homomorphic encryption (HE) enables computation on encrypted data, thus, providing cryptographic guarantees to protect privacy. But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer type prediction using a dataset consisting of more than 2 million genetic mutations from 2713 patients for several cancer types by building a highly accurate ML model and then implementing its privacy preserving version in HE. Our solution for cancer type inference encodes somatic mutations based on their impact on the cancer genomes into the feature space and then uses statistical tests for feature selection. We propose a fast matrix multiplication algorithm for HE-based model. Our final model achieves 0.98 micro-average area under curve improving accuracy from 70.08 to 83.61% , being 550 times faster than the standard matrix multiplication-based privacy-preserving models. Our tool can be found at https://github.com/momalab/octal-candet.

Cloud Multimedia Data Security by Optimization-Assisted Cryptographic Technique

Currently, the size of multimedia data is rising gradually from gigabytes to petabytes, due to the progression of a larger quantity of realistic data. The majority of big data is conveyed via the internet and they were accumulated on cloud servers. Since cloud computing offers internet-oriented services, there were a lot of attackers and malevolent users. They always attempt to deploy the private data of users without any right access. At certain times, they substitute the real data by any counterfeit data. As a result, data protection has turned out to be a noteworthy concern in recent times. This paper aims to establish an optimization-based privacy preservation model for preserving multimedia data by selecting the optimal secret key. Here, the encryption and decryption process is carried out by Improved Blowfish cryptographic technique, where the sensitive data in cloud server is preserved using the optimal key. Optimal key generation is the significant procedure to ensure the objectives of integrity and confidentiality. Likewise, data restoration is the inverse process of sanitization (decryption). In both the cases, key generation remains a major aspect, which is optimally chosen by a novel hybrid algorithm termed as “Clan based Crow Search with Adaptive Awareness probability (CCS-AAP). Finally, an analysis is carried out to validate the improvement of the proposed method.

Differentially Private Timestamps Publishing in Trajectory

In recent years, location-based social media has become popular, and a large number of spatiotemporal trajectory data have been generated. Although these data have significant mining value, they also pose a great threat to the privacy of users. At present, many studies have realized the privacy-preserving mechanism of location data in social media in terms of data utility and privacy preservation, but rarely have any of them considered the correlation between timestamps and geographical location. To solve this problem, in this paper, we first propose a k-anonymity-based mechanism to hide the user’s specific time segment during a single day, and then propose an optimized truncated Laplacian mechanism to add noise to each data grid (the frequency of time data) of the anonymized time distribution. The time data after secondary processing are fuzzy and uncertain, which not only protects the privacy of the user’s geographical location from the time dimension but also retains a certain value of data mining. Experiments on real datasets show that the TDP privacy-preserving model has good utility.

Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

Online Social Networks (OSN) sites allow end-users to share a great deal of information, which may also contain sensitive information, that may be subject to commercial or non-commercial privacy attacks. As a result, guaranteeing various levels of privacy is critical while publishing data by OSNs. The clustering-based solutions proved an effective mechanism to achieve the privacy notions in OSNs. But fixed clustering limits the performance and scalability. Data utility degrades with increased privacy, so balancing the privacy utility trade-off is an open research issue. The research has proposed a novel privacy preservation model using the enhanced clustering mechanism to overcome this issue. The proposed model includes phases like pre-processing, enhanced clustering, and ensuring privacy preservation. The enhanced clustering algorithm is the second phase where authors modified the existing fixed k-means clustering using the threshold approach. The threshold value is determined based on the supplied OSN data of edges, nodes, and user attributes. Clusters are k-anonymized with multiple graph properties by a novel one-pass algorithm. After achieving the k-anonymity of clusters, optimization was performed to achieve all privacy models, such as k-anonymity, t-closeness, and l-diversity. The proposed privacy framework achieves privacy of all three network components, i.e., link, node, and user attributes, with improved utility. The authors compare the proposed technique to underlying methods using OSN Yelp and Facebook datasets. The proposed approach outperformed the underlying state of art methods for Degree of Anonymization, computational efficiency, and information loss.

A Privacy-Preserving System Design for Digital Presence Protection

A person’s privacy has become a growing concern, given the nature of an expansive reliance on real-time video activities with video capture, stream, and storage. This paper presents an innovative system design based on a privacy-preserving model. The proposed system design is implemented by employing an enhanced capability that overcomes today’s single parameter-based access control protection mechanism for digital privacy preservation. The enhanced capability combines multiple access control parameters: facial expression, resource, environment, location, and time. The proposed system design demonstrated that a person’s facial expressions combined with a set of access control rules can achieve a person’s privacy-preserving preferences. The findings resulted in different facial expressions successfully triggering a person’s face to be blurred and a person’s privacy when using a real-time video conferencing service captured from a webcam or virtual webcam. A comparison analysis of capabilities between existing designs and the proposed system design shows enhancement of the capabilities of the proposed system. A series of experiments exercising the enhanced, real-time multi-parameter-based system was shown as a viable path forward for preserving a person’s privacy while using a webcam or virtual webcam to capture, stream, and store videos.

An Extensive Study and Review of Privacy Preservation Models for the Multi-Institutional Data

An Extensive Study and Review of Privacy Preservation Models for the Multi-Institutional Data

Research on personalised privacy-preserving model of multi-sensitive attributes

Research on personalised privacy-preserving model of multi-sensitive attributes

Research on personalised privacy-preserving model of multi-sensitive attributes

Research on personalised privacy-preserving model of multi-sensitive attributes

A Novel Secure and Privacy-Preserving Model for OpenID Connect Based on Blockchain

A Novel Secure and Privacy-Preserving Model for OpenID Connect Based on Blockchain

Study and Analysis of Various Cloud Security, Authentication, and Data Storage Models

In recent days, widespread acceptance of cloud data storage applications increases various privacy problems and security problems. Outsourced data security is considered the main confrontation for cloud clients because of data control loss. This review presents a detailed survey of 50 research papers presenting privacy preservation approaches, namely authentication-based, cloud security-based, data storage-based, data security-based, and encryption-based techniques. The analysis is considered based on the categorization of approaches, dataset employed, utilized software tools, published year, and the performance metrics are discussed. Furthermore, problems raised in existing privacy preservation techniques are elucidated in the research gaps and problems section. The future work of this study is based on the research gaps and problems recognized from present research schemes. Additionally, JAVA software language is widely utilized for implementing privacy preservation models, and the Amazon access sample database is a commonly employed dataset for the privacy preservation approach.