scholarly journals A Credential Data Privacy Preserving in web Environment using Secure Data Contribution Retrieval Algorithm

2019 ◽  
Vol 10 (3) ◽  
pp. 102
Author(s):  
Kumaran U ◽  
Neelu Khare
Keyword(s):  
Data Privacy ◽  
Privacy Preserving ◽  
Retrieval Algorithm ◽  
Web Environment ◽  
Secure Data

scholarly journals EPPDA: An Efficient and Privacy-Preserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Faris A. Almalki ◽  
Ben Othman Soufiene
Keyword(s):  
Data Aggregation ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Data Integrity ◽  
Privacy Preserving ◽  
Healthcare Applications ◽  
Healthcare Data ◽  
Secure Data ◽  
Aggregation Scheme ◽  
Secure Data Aggregation

Nowadays, IoT technology is used in various application domains, including the healthcare, where sensors and IoT enabled medical devices exchange data without human interaction to securely transmit collected sensitive healthcare data towards healthcare professionals to be reviewed and take proper actions if needed. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. In healthcare applications, many miniaturized devices are exploited for healthcare data collection and transmission. Thus, there is a need for secure data aggregation while preserving the data integrity and privacy of the patient. For that, the security, privacy, and aggregation of health data are very important aspects to be considered. This paper proposes a novel secure data aggregation scheme called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation. EPPDA, also, uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The major advantage of homomorphic encryption is allowing complex mathematical operations to be performed on encrypted data without knowing the contents of the original plain data. The proposed system is developed using MySignals HW V2 platform. Security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

Optimal Cryptography Scheme and Efficient Neutrosophic C-Means Clustering for Anomaly Detection in Cloud Environment

2020 ◽  
pp. 2150084
Author(s):  
R. Barona ◽  
E. A. Mary Anita
Keyword(s):  
Anomaly Detection ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Data Partitioning ◽  
Public Key ◽  
Sensor Data ◽  
Grey Wolf Optimizer ◽  
Detection Accuracy ◽  
Secure Data ◽  
Cryptographic Scheme

This paper introduces an efficient and scalable cloud-based privacy preserving model using a new optimal cryptography scheme for anomaly detection in large-scale sensor data. Our proposed privacy preserving model has maintained a better tradeoff between reliability and scalability of the cloud computing resources by means of detecting anomalies from the encrypted data. Conventional data analysis methods have used complex and large numerical computations for the anomaly detection. Also, a single key used by the symmetric key cryptographic scheme to encrypt and decrypt the data has faced large computational complexity because the multiple users can access the original data simultaneously using this single shared secret key. Hence, a classical public key encryption technique called RSA is adopted to perform encryption and decryption of secure data using different key pairs. Furthermore, the random generation of public keys in RSA is controlled in the proposed cloud-based privacy preserving model through optimizing a public key using a new hybrid local pollination-based grey wolf optimizer (LPGWO) algorithm. For ease of convenience, a single private server handling the organization data within a collaborative public cloud data center when requiring the decryption of secure sensor data are allowed to decrypt the optimal secure data using LPGWO-based RSA optimal cryptographic scheme. The data encrypted using an optimal cryptographic scheme are then encouraged to perform data clustering computations in collaborative public servers of cloud platform using Neutrosophic c-Means Clustering (NCM) algorithm. Hence, this NCM algorithm mainly focuses for data partitioning and classification of anomalies. Experimental validation was conducted using four datasets obtained from Intel laboratory having publicly available sensor data. The experimental outcomes have proved the efficiency of the proposed framework in providing data privacy with high anomaly detection accuracy.

scholarly journals Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Qi Dou ◽  
Tiffany Y. So ◽  
Meirui Jiang ◽  
Quande Liu ◽  
Varut Vardhanabhuti ◽  
...  
Keyword(s):  
Data Privacy ◽  
Medical Training ◽  
External Validation ◽  
Mainland China ◽  
Privacy Preserving ◽  
Generalization Capability ◽  
Major Focus ◽  
Sensitive Data ◽  
Lung Abnormalities ◽  
Medical Image Diagnosis

AbstractData privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

scholarly journals Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hua Dai ◽  
Hui Ren ◽  
Zhiye Chen ◽  
Geng Yang ◽  
Xun Yi
Keyword(s):  
Data Privacy ◽  
Logistic Map ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Service Recommendation ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Sorting Algorithms ◽  
Common Operation ◽  
Cloud Servers

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

Privacy-preserving public auditing for secure data storage in fog-to-cloud computing

2019 ◽  
Vol 127 ◽  
pp. 59-69 ◽  
Author(s):  
Hui Tian ◽  
Fulin Nan ◽  
Chin-Chen Chang ◽  
Yongfeng Huang ◽  
Jing Lu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Privacy Preserving ◽  
Public Auditing ◽  
Secure Data

scholarly journals Secure Data-Encryption Strategy for Big-Data in Mobile Cloud

2019 ◽  
Vol 9 (1S) ◽  
pp. 1-4
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Data Encryption ◽  
Mobile Cloud ◽  
Timing Constraints ◽  
Privacy And Security ◽  
Secure Data ◽  
Consistent Performance ◽  
A New Technique ◽  
High Level

Now-a-days data plays a key role in Information Technology and while coming to privacy of that data it has become a considerable issue to maintain data security at high level. Large amounts of data generated through devices are considered as a major obstacle and also tough to handle in real time scenarios. To meetwith consistent performance applications at present abandon encryptions techniquesbecausethe time for the execution and the completion of encryption techniques plays a key role during processing and transmissions of data. In this paper our moto is to secure data and proposed a new technique called Dynamic Data Encryption Strategy (DDES)which selectively encrypts data and uses some algorithms which provides a perfect encryption strategy for the data packages under some timing constraints. By this method we can achieve data privacy and security for big-data in mobile cloud-computing by using an encryption strategy respective to their requirements during execution time.

A Generic Privacy Breach Preventing Methodology for Cloud Based Web Service

2015 ◽  
pp. 426-458 ◽  
Author(s):  
S. R. Murugaiyan ◽  
D. Chandramohan ◽  
T. Vengattaraman ◽  
P. Dhavachelvan
Keyword(s):  
Private Information ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Digital Data ◽  
Cloud Data ◽  
Privacy Breach ◽  
Cloud Data Storage ◽  
User Data ◽  
Information Technology Development ◽  
User Data Privacy

The present focuses on the Cloud storage services are having a critical issue in handling the user's private information and its confidentiality. The User data privacy preserving is a vital facet of online storage in cloud computing. The information in cloud data storage is underneath, staid molests of baffling addict endeavor, and it may leads to user clandestine in a roar privacy breach. Moreover, privacy preservation is an indeed research pasture in contemporary information technology development. Preserving User Data in Cloud Service (PUDCS) happens due to the data privacy breach results to a rhythmic way of intruding high confidential digital storage area and barter those information into business by embezzle others information. This paper focuses on preventing (hush-hush) digital data using the proposed privacy preserving framework. It also describes the prevention of stored data and de-identifying unauthorized user attempts, log monitoring and maintaining it in the cloud for promoting allusion to providers and users.

Privacy-Preserving Outsourced Similarity Search

2014 ◽  
Vol 25 (3) ◽  
pp. 48-71 ◽  
Author(s):  
Stepan Kozak ◽  
David Novak ◽  
Pavel Zezula
Keyword(s):  
Similarity Search ◽  
Data Privacy ◽  
System Analysis ◽  
Keyword Search ◽  
Evaluation Criteria ◽  
Similarity Index ◽  
Data Retrieval ◽  
Privacy Preserving ◽  
Scientific Data ◽  
Sensitive Data

The general trend in data management is to outsource data to 3rd party systems that would provide data retrieval as a service. This approach naturally brings privacy concerns about the (potentially sensitive) data. Recently, quite extensive research has been done on privacy-preserving outsourcing of traditional exact-match and keyword search. However, not much attention has been paid to outsourcing of similarity search, which is essential in content-based retrieval in current multimedia, sensor or scientific data. In this paper, the authors propose a scheme of outsourcing similarity search. They define evaluation criteria for these systems with an emphasis on usability, privacy and efficiency in real applications. These criteria can be used as a general guideline for a practical system analysis and we use them to survey and mutually compare existing approaches. As the main result, the authors propose a novel dynamic similarity index EM-Index that works for an arbitrary metric space and ensures data privacy and thus is suitable for search systems outsourced for example in a cloud environment. In comparison with other approaches, the index is fully dynamic (update operations are efficient) and its aim is to transfer as much load from clients to the server as possible.

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