Narrow Results

Conventional biometric systems are vulnerable to a range of harmful threats and privacy violations, putting the users who have registered with them in grave danger. Therefore, there is a need to develop a Privacy-Preserving and Authenticating Framework for Biometric-based Systems (PPAF-BS) that allows users to access multiple applications while also protecting their privacy. There are various existing works on biometric-based systems, but most of them do not address privacy concerns. Conventional biometric systems require the storage of biometric data, which can be easily accessed by attackers, leading to privacy violations. Some research works have used differential privacy techniques to address this issue, but they have not been widely applied in biometric-based systems. The existing biometric-based systems have a significant privacy concern, and there is a lack of privacy-preserving techniques in such systems. Therefore, there is a need to develop a PPAF-BS that can protect the user’s privacy and maintain the system’s efficiency. The proposed method uses Hybrid Deep Learning (HDL) with palmprint, ear, and face biometric features for person identification. Additionally, Discrete Cosine Transform (DCT) feature transformation and Lagrange’s interpolation-based image transformation are used as part of the authentication scheme. Sensors are used to record three biometric traits: palmprint, ear, and face. The combination of biometric characteristics provides an accuracy of 96.4% for the $8\times 8$ image size. The proposed LI-based image transformation lowers the original $512\times 512$ pixels to an $8\times 8$ hidden pattern. This drastically decreases the database size, thereby reducing storage needs. The proposed method offers a safe authentication system with excellent accuracy, a fixed-size database, and the privacy protection of multi-modal biometric characteristics without sacrificing overall system efficiency. The system achieves an accuracy of 96.4% for the $8\times 8$ image size, and the proposed LI-based picture transformation significantly reduces the database size, which is a significant achievement in terms of storage requirements. Therefore, the proposed method can be considered an effective solution to the privacy and security concerns of biometric-based systems.

Remote health data monitoring to achieve intelligent healthcare has recently drawn a lot of interest due to the Internet of Things’ (IoT) substantially increased implementation. However, due to the constrained processing and storage capabilities of IoT devices, users’ health data are typically stored in a centralized third party, such as a hospital database or cloud, and this causes users to lose control of their health data, which is easily the cause of privacy leakage and a single-point bottleneck. A medical data transmission and preservation strategy is proposed which is based on the hospital’s private block chain in order to enhance the electronic health system. The three major phases of the privacy-preserving medical data strategy initiated in this study are “data sanitization, optimal key generation, and data restoration”. Prior to being added to the block–blocks chains, the medical record is cleaned up. The sanitization phase will employ the improved association rule concealment technique. The cleaned content is recovered at the receiver’s end. More significantly, both methods heavily rely on optimal key creation, with the ideal key being selected using a new hybrid optimization model called Dragonfly-Updated Elephant Herding Optimization (DUEHO). Elephant Herding Optimization (EHO) and the traditional Dragonfly Algorithm (DA) are conceptually combined in the proposed DUEHO. At the end, proposed model’s performance is compared over existing techniques concerning various metrics. The convergence rate of the suggested model is 1.05%, 0.31%, 0.32%, 0.4%, 0.43%, 0.45% better than the existing models like FF, MFO, GWO, WOA, DA and EHO, respectively.

Problem background: The biggest problem in the medical field is data security and privacy. Enhanced protection of data is required as a result of the implementation of increasing laws and regulations, the utilization of electronic patient records, the reorganization of providers, and the growing necessitate for data across patients, taxpayers, and physicians. Several standard methods of anonymization are more effective in sanitizing data, yet they are unsuccessful in restoring the data. Although a few privacy preservation algorithms have been established in recent days, the accuracy of maintaining both private and insensitive information seems to be relatively low. Objective: The aim is to develop a novel system for privacy preservation. Here, the encryption process is done using the Information Entropy-based Adaptive Encryption Technique (IEAET). The health data are gathered from appropriate online sources. The sensitive and insensitive attributes are distinguished with the help of Information Entropy. From these data, the sensitive attributes are analyzed by the information entropy optimally selected using the Enhanced Walrus Optimization Algorithm (EWaOA). Then the sensitive attributes are encrypted using the Multi-scheme Fully Homomorphic Encryption (Multi-scheme FHE) approach. Here, the key required for encryption is optimally generated using the proposed EWaOA, and this key is utilized for the encryption of sensitive data with high integrity. Finally, the insensitive attributes are enciphered using the Rivest–Shamir–Adleman (RSA) algorithm. Therefore, the health data are preserved with more security without any information loss. Result: The outcome of the proposed method provided the key variation of restoration efficiency is 98.5%. Discussion: The developed model provides better restoration efficiency by using an optimization algorithm to optimally select the attributes. It provides more effective outcomes and security, and is well performed in healthcare data preservation to enhance the privacy of data. The developed model is used for reducing the risk and saving the cost. Thus, it proved that the developed model significantly outperformed conventional methods and its reliability was also improved.

The objective of the research work is to analyze and validate health records and securing the personal information of patients is a challenging issue in health records mining. The risk prediction task was formulated with the label Cause of Death (COD) as a multi-class classification issue, which views health-related death as the “biggest risk.” This unlabeled data particularly describes the health conditions of the participants during the health examinations. It can differ tremendously between healthy and highly ill. Besides, the problems of distributed secure data management over privacy-preserving are considered. The proposed health record mining is in the following stages. In the initial stage, effective features such as fisher score, Pearson correlation, and information gain is calculated from the health records of the patient. Then, the average values are calculated for the extracted features. In the second stage, feature selection is performed from the average features by applying the Euclidean distance measure. The chosen features are clustered in the third stage using distance adaptive fuzzy c-means clustering algorithm (DAFCM). In the fourth stage, an entropy-based graph is constructed for the classification of data and it categorizes the patient’s record. At the last stage, for security, privacy preservation is applied to the personal information of the patient. This performance is matched against the existing methods and it gives better performance than the existing ones.

Road side units (RSUs) can act as fog nodes to perform data aggregation at the edge of network, which can reduce communication overhead and improve the utilization of network resources. However, because the RSU is public infrastructure, this feature may bring data security and privacy risks in data aggregation. In this paper, we propose a secure multi-subinterval data aggregation scheme, named SMDA, with interval privacy preservation for vehicle sensing systems. Specifically, our scheme combines the $1\text{-}\text{R}$ encoding theory and proxy re-encryption to protect interval privacy, this can ensure that the interval information is only known by the data center, and the RSU can classify the encrypted data without knowing the plaintext of the data and interval information. Meanwhile, our scheme employs the Paillier homomorphic encryption to accomplish data aggregation at the RSU, and the Identity-based batch authentication technology to solve authentication and data integrity. Finally, the security analysis and performance evaluations illustrate the safety and efficiency of our scheme.

In the current research on data query for two-tiered WSN, the privacy-preservation range query is one of the hotspots. However, there are some problems in the existing researches in two-tiered wireless sensor networks such as high computational and communication costs for security comparison items and high energy consumption of sensing nodes. In this paper, a privacy-preservation range query protocol based on the integration reversal 0-1 encoding with Bloom filter is researched and designed. In the sensing data submission stage, the optimized reversal 0-1 encoding, HMAC algorithm, AES encryption algorithm and variable-length Bloom filter are used for generating the maximum–minimum comparison encoding and constructing a shorter verification index chain to reduce computational and communication costs of sensing nodes; in the private data range query stage, the base station uses the HMAC algorithm to convert the plaintext query range into the ciphertext query range and sends it to the storage node. In the storage node, the bitmap encoding information of the verification index chain is calculated with the comparison rule of the reversal 0-1 encoding and it is returned to the base station together with the verification index chain and the data ciphertext that compliance with the query rule; in the data integrity verification stage, the integrity of the query results using the verification index chain and bitmap encoding is verified at the base station. In the experimental section, the Cortex-M4 development board equipped with the Alios-Things operating system as sensing node and the Cortex-A9 development board equipped with the Linux operating system as storage node are implemented in this protocol, which is compared with the existing protocols in three aspects: the number of data collected in each cycle, the length of data and the number of data dimensions. The experimental results show that the energy consumption of this protocol is lower under the same experimental environment.

As a special type of location-based service (LBS), crowdsensing becomes more prosperous in people’s daily life. However, during the process of task distribution, the publisher’s and workers’ locations will be revealed to each other, and then their personal privacy is violated. So in this paper, in order to cope with the violation of location privacy in crowdsensing and provide privacy preservation service for both entities, an active oblivious transfer-based location privacy preservation crowdsensing scheme (short for AOTC) has been proposed. In this scheme, the oblivious transfer is used to encrypt the range of sensing grid of workers, and then matching sensing grids with the sensing region of the publisher without decryption. During the whole process, the process of location matching and results sending is disposed of by the entity of workers actively, so does not establish any data aggregation that can be used as the point of attack. As a result, the AOTC can guarantee the personal privacy of both entities in crowdsensing cannot be obtained by each other, and guarantee other workers also difficult to obtain the precise location of any workers. In addition, as workers send the sensing result to the publisher actively this scheme can also increase the probability of workers’ participation potentially. At last, the theoretical privacy preservation ability of AOTC is analyzed in the section on security analysis with three types of privacy threats. Then the performance of AOTC is compared with other similar schemes in both privacy preservation and execution efficiency, so in simulation experiments, comparison results with brief analyses will confirm that the AOTC has achieved the desired effect and will further demonstrate the superiority.

Due to the proliferation of online social networking, a large number of personal data are publicly available. As such, personal attacks, reputational, financial, or family losses might occur once this personal and sensitive information falls into the hands of malicious hackers. Research on Privacy-Preserving Network Publishing has attracted much attention in recent years. But most work focus on node de-identification and link protection. In academic social networks, business transaction networks, and transportation networks, etc, node identities and link structures are public knowledge but weights and shortest paths are sensitive. In this work, we study the problem of k-anonymous path privacy. A published network graph with k-anonymous path privacy has at least k indistinguishable shortest paths between the source and destination vertices [21]. In order to achieve such privacy, three different strategies of modification on edge weights of directed graphs are proposed. Numerical comparisons show that weight-proportional-based strategy is more efficient than PageRank-based and degree-based strategies. In addition, it is also more efficient and causes less information loss than running on un-directed graphs.

One of the emerging technologies, seeking significant attention in the research area is cloud computing. However, privacy is the major concern in the cloud, as it is essential to manage the confidentiality in the data shared. In the first work, the privacy preservation model was developed by newly designed Kronecker product based Bat algorithm. Here, the previous work is extended by developing the classification algorithm for classifying the privacy preserved database. Initially, the Kronecker product based Bat algorithm finds the privacy preserved database from the original medical data. Then, the ontology based features are extracted from the privacy preserved database and given to the data classifier. Here, a classifier, named Whale based Sine Cosine Algorithm with Support Vector Neural Network (WSCA-SVNN), is newly developed for the data classification. The proposed WSCA algorithm helps in optimally choosing the weights for SVNN classifier, and finally, the WSCA-SVNN classifier classifies the medical data. The simulation of the proposed privacy preserved data classification network is done by utilizing the heart disease database. The analysis shows that the proposed WSCA-SVNN classifier scheme achieved an accuracy value of 90.29% during medical data classification.

The IT security paradigm evolves from secret-based to biometric identity-based. Biometric identification has gradually become more popular in recent years for handheld devices. Privacy-preserving is a key concern when biometrics is used in authentication systems in the present world today. Nowadays, the declaration of biometric traits has been imposed not only by the government but also by many private entities. There are no proper mechanisms and assurance that biometric traits will be kept safe by such entities. The encryption of biometric traits to avoid privacy attacks is a giant problem. Hence, state-of-the-art safety and security technological solutions must be devised to prevent the loss and misuse of such biometric traits. In this paper, we have identified different cancelable biometrics methods with the possible attacks on the biometric traits and directions on possible countermeasures in order to design a secure and privacy-preserving biometric authentication system. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation and Index of max hashing. We tested and evaluated the proposed novel method on a standard dataset and achieved good results.

Cloud security in finance is considered as the key importance, taking account of the aspect of critical data stored over cloud spaces within organizations all around the globe. They are chiefly relying on cloud computing to accelerate their business profitability and scale up their business processes with enhanced productivity coming through flexible work environments offered in cloud-run working systems. Hence, there is a prerequisite to contemplate cloud security in the entire financial service sector. Moreover, the main issue challenged by privacy and security is the presence of diverse chances to attack the sensitive data by cloud operators, which leads to double the user’s anxiety on the stored data. For solving this problem, the main intent of this paper is to develop an intelligent privacy preservation approach for data stored in the cloud sector, mainly the financial data. The proposed privacy preservation model involves two main phases: (a) data sanitization and (b) data restoration. In the sanitization process, the sensitive data is hidden, which prevents sensitive information from leaking on the cloud side. Further, the normal as well as the sensitive data is stored in a cloud environment. For the sanitization process, a key should be generated that depends on the new meta-heuristic algorithm called crossover improved-lion algorithm (CI-LA), which is inspired by the lion’s unique social behavior. During data restoration, the same key should be used for effectively restoring the original data. Here, the optimal key generation is done in such a way that the objective model involves the degree of modification, hiding rate, and information preservation rate, which effectively enhance the cyber security performance in the cloud.

The quick emergence in the quantity of data produced through the linked devices of Internet of Things (IoT) models opened the novel potential to improve service qualities for budding tools considering data sharing. However, privacy problems are main issues of data providers for sharing data. The outflow of confidential data causes severe problems beyond the loss in finance of providers. A blockchain-based secured data-sharing model is devised for dealing with various kinds of parties. Thus, data-sharing issue is modeled as a machine learning issue by adapting federated learning (FL). Here, data privacy is controlled by sharing data in spite of exposing genuine data. At last, the FL is combined in consensus task of permissioned blockchain for accomplishing federated training. Here, the data model learning is executed using a deep maxout network (DMN), which is trained using jellyfish search African vultures optimization (JSAVO). Moreover, the data-sharing records are generated to share data amid data providers and requestors. The proposed JSAVO-based DMN outperformed with better accuracy of 93.3%, FPR of 0.054, loss function of 0.067, mean square error (MSE) of 0.346, mean average precision of 94.6, RMSE of 0.589, computational time of 17.47s, and memory usage of 48.62MB.

Due to widespread growth of cloud technology, virtual server accomplished in cloud platform may collect useful data from a client and then jointly disclose the client’s sensitive data without permission. Hence, from the perspective of cloud clients, it is very important to take confident technical actions to defend their privacy at client side. Accordingly, different privacy protection techniques have been presented in the literature for safeguarding the original data. This paper presents a technique for privacy preservation of cloud data using Kronecker product and Bat algorithm-based coefficient generation. Overall, the proposed privacy preservation method is performed using two important steps. In the first step, PU coefficient is optimally found out using PUBAT algorithm with new objective function. In the second step, input data and PU coefficient is then utilized for finding the privacy protected data for further data publishing in cloud environment. For the performance analysis, the experimentation is performed with three datasets namely, Cleveland, Switzerland and Hungarian and evaluation is performed using accuracy and DBDR. From the outcome, the proposed algorithm obtained the accuracy of 94.28% but the existing algorithm obtained only the 83.64% to prove the utility. On the other hand, the proposed algorithm obtained DBDR of 35.28% but the existing algorithm obtained only 12.89% to prove the privacy measure.