scholarly journals Quasi-Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Huda O. Mansour ◽  
Maheyzah M. Siraj ◽  
Fuad A. Ghaleb ◽  
Faisal Saeed ◽  
Eman H. Alkhammash ◽  
...  
Keyword(s):  
Privacy Preservation ◽  
Recognition Algorithm ◽  
Mining Operations ◽  
Cloud Data ◽  
Privacy Leakage ◽  
Resource Limited ◽  
Outsourced Data ◽  
Identity Disclosure ◽  
Sensor Cloud ◽  
Risk Rate

Cloud computing plays an essential role as a source for outsourcing data to perform mining operations or other data processing, especially for data owners who do not have sufficient resources or experience to execute data mining techniques. However, the privacy of outsourced data is a serious concern. Most data owners are using anonymization-based techniques to prevent identity and attribute disclosures to avoid privacy leakage before outsourced data for mining over the cloud. In addition, data collection and dissemination in a resource-limited network such as sensor cloud require efficient methods to reduce privacy leakage. The main issue that caused identity disclosure is quasi-identifier (QID) linking. But most researchers of anonymization methods ignore the identification of proper QIDs. This reduces the validity of the used anonymization methods and may thus lead to a failure of the anonymity process. This paper introduces a new quasi-identifier recognition algorithm that reduces identity disclosure which resulted from QID linking. The proposed algorithm is comprised of two main stages: (1) attribute classification (or QID recognition) and (2) QID dimension identification. The algorithm works based on the reidentification of risk rate for all attributes and the dimension of QIDs where it determines the proper QIDs and their suitable dimensions. The proposed algorithm was tested on a real dataset. The results demonstrated that the proposed algorithm significantly reduces privacy leakage and maintains the data utility compared to recent related algorithms.

scholarly journals What If Keys Are Leaked? towards Practical and Secure Re-Encryption in Deduplication-Based Cloud Storage

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 142
Author(s):  
Weijing You ◽  
Lei Lei ◽  
Bo Chen ◽  
Limin Liu
Keyword(s):  
Experimental Evaluation ◽  
Cloud Storage ◽  
Security Analysis ◽  
Encrypted Data ◽  
Cloud Data ◽  
Storage Cost ◽  
Outsourced Data ◽  
Proof Of Ownership ◽  
Client Side ◽  
Over Time

By only storing a unique copy of duplicate data possessed by different data owners, deduplication can significantly reduce storage cost, and hence is used broadly in public clouds. When combining with confidentiality, deduplication will become problematic as encryption performed by different data owners may differentiate identical data which may then become not deduplicable. The Message-Locked Encryption (MLE) is thus utilized to derive the same encryption key for the identical data, by which the encrypted data are still deduplicable after being encrypted by different data owners. As keys may be leaked over time, re-encrypting outsourced data is of paramount importance to ensure continuous confidentiality, which, however, has not been well addressed in the literature. In this paper, we design SEDER, a SEcure client-side Deduplication system enabling Efficient Re-encryption for cloud storage by (1) leveraging all-or-nothing transform (AONT), (2) designing a new delegated re-encryption (DRE), and (3) proposing a new proof of ownership scheme for encrypted cloud data (PoWC). Security analysis and experimental evaluation validate security and efficiency of SEDER, respectively.

scholarly journals CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning

2020 ◽  
Author(s):  
Yuanrui Dong ◽  
Peng Zhao ◽  
Hanqiao Yu ◽  
Cong Zhao ◽  
Shusen Yang
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Fine Tuning ◽  
Quantization Scheme ◽  
Raw Data ◽  
Resource Limited ◽  
Lower Accuracy ◽  
Bandwidth Consumption

The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, response latency, and data privacy preservation. Focusing on bandwidth efficient edge-cloud collaborative training of DNN-based classifiers, we present CDC, a Classification Driven Compression framework that reduces bandwidth consumption while preserving classification accuracy of edge-cloud collaborative DL. Specifically, to reduce bandwidth consumption, for resource-limited edge servers, we develop a lightweight autoencoder with a classification guidance for compression with classification driven feature preservation, which allows edges to only upload the latent code of raw data for accurate global training on the Cloud. Additionally, we design an adjustable quantization scheme adaptively pursuing the tradeoff between bandwidth consumption and classification accuracy under different network conditions, where only fine-tuning is required for rapid compression ratio adjustment. Results of extensive experiments demonstrate that, compared with DNN training with raw data, CDC consumes 14.9× less bandwidth with an accuracy loss no more than 1.06%, and compared with DNN training with data compressed by AE without guidance, CDC introduces at least 100% lower accuracy loss.

Secure Data Validation and Transmission In Cloud and IoT Through Ban Logic And KP-ABE

2021 ◽  
Vol 11 ◽  
Author(s):  
Narander Kumar ◽  
Jitendra Kumar Samriya
Keyword(s):  
Privacy Preservation ◽  
Data Science ◽  
User Authentication ◽  
Ban Logic ◽  
Data Validation ◽  
Security Measures ◽  
Privacy And Security ◽  
Cloud Data ◽  
Control Approach ◽  
User Data

Background: Cloud computing is a service that is being accelerating its growth in the field of information technology in recent years. Privacy and security are challenging issues for cloud users and providers. Obective: This work aims at ensuring secured validation of user and protects data during transmission for users in a public IoT-cloud environment. Existing security measures however fails by their single level of security, adaptability for large amount of data and reliability. Therefore, to overcome these issues and to achieve a better solution for vulnerable data. Method: The suggested method utilizes a secure transmission in cloud using key policy attribute based encryption (KPABE). Initially, user authentication is verified. Then the user data is encrypted with the help of KP-ABE algorithm. Finally, data validation and privacy preservation are done by Burrows-Abadi-Needham (BAN) logic. This verified, and shows that the proposed encryption is correct, secure and efficient to prevent unauthorized access and prevention of data leakage so that less chances of data/identity, theft of a user is the analysis and performed by KP-ABE, that is access control approach. Results: Here the method attains the maximum of 88.35% of validation accuracy with a minimum 8.78ms encryption time, which is better when, compared to the existing methods. The proposed mechanism is done by MATLAB. The performance of the implemented method is calculated based on the time of encryption and decryption, execution time and validation accuracy. Conclusion: Thus the proposed approach attains the high IoT-cloud data security and increases the speed for validation and transmission with high accuracy and used for cyber data science processing.

Validation of Sentinel-5p TROPOMI cloud data with ground-based Cloudnet and other satellite data products

2021 ◽  
Author(s):  
Steven Compernolle ◽  
Athina Argyrouli ◽  
Ronny Lutz ◽  
Maarten Sneep ◽  
Jean-Christopher Lambert ◽  
...  
Keyword(s):  
Major Change ◽  
Recognition Algorithm ◽  
Atmospheric Composition ◽  
Trace Gas ◽  
Oxygen Absorption ◽  
Absorption Bands ◽  
Cloud Data ◽  
Cloud Height ◽  
Data Products ◽  
Retrieval Scheme

<p>Space-born atmospheric composition measurements, like those from Sentinel-5p TROPOMI, are strongly affected by the presence of clouds. Dedicated cloud data products, typically retrieved with the same sensor, are therefore an important tool for the provider of atmospheric trace gas retrievals. Cloud products are used for filtering and modification of the modelled radiative transfer.</p><p>In this work, we assess the quality of the cloud data derived from Copernicus Sentinel-5 Precursor TROPOMI radiance measurements. Three cloud products are considered: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (same; Clouds-as Reflecting Boundaries), and (iii) the S5p support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands for Sentinel). These cloud products are used in the retrieval of several S5p trace gas products (e.g., ozone columns and profile, total and tropospheric nitrogen dioxide, sulfur dioxide, formaldehyde). The quality assessment of these cloud products is carried out within the framework of ESA’s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective atmospheric gases.</p><p>Cloud height data from the three S5p cloud products is compared to radar/lidar based cloud profile information from the ground-based networks CLOUDNET and ARM. The cloud height from S5p CLOUD CRB and S5p FRESCO are on average 0.6 km below the cloud mid-height of CLOUDNET measurements, and the cloud top height from S5p CLOUD CAL is on average 1 km below CLOUDNET’s cloud top height. However, the comparison is different for low and high clouds, with S5p CLOUD CAL cloud top height being only 0.3 km below CLOUDNET’s for low clouds.  The radiometric cloud fraction and cloud (top) height are compared to those of other satellite cloud products like Aura OMI O<sub>2</sub>-O<sub>2</sub>. While the latitudinal variation is often similar, offsets are encountered.</p><p>Recently, major S5p cloud product upgrades were released for S5p OCRA/ROCINN (July 2020) and for S5p FRESCO (December 2020), leading to a decrease of the ROCINN CRB cloud height and an increase of the FRESCO cloud height on average. Moreover, a major change in the ROCINN surface albedo treatment leads to a clear improvement of the comparison with CLOUDNET at the complicated sea/land/ice/snow site Ny-Alesund.</p><div></div>

Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

2021 ◽  
pp. 840-856
Author(s):  
Feng Xu ◽  
Mingming Su ◽  
Yating Hou
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
The Internet ◽  
Malicious Nodes ◽  
Privacy Leakage ◽  
Computing Paradigm ◽  
Data Release ◽  
Security Levels ◽  
Simulation Results

The Cloud computing paradigm can improve the efficiency of distributed computing by sharing resources and data over the Internet. However, the security levels of nodes (or severs) are not the same, thus, sensitive tasks and personal data may be scheduled (or shared) to some unsafe nodes, which can lead to privacy leakage. Traditional privacy preservation technologies focus on the protection of data release and process of communication, but lack protection against disposing sensitive tasks to untrusted computing nodes. Therefore, this article put forwards a protocol based on task-transformation, by which tasks will be transformed into another form in the task manager before they can be scheduled to other nodes. The article describes a privacy preservation algorithm based on separation sensitive attributes from values (SSAV) to realize the task-transformation function. This algorithm separates sensitive attributes in the tasks from their values, which make the malicious nodes cannot comprehend the real meaning of the values even they get the transformed tasks. Analysis and simulation results show that the authors' algorithm is more effective.

Semantic Multi-Keyword Search over Encrypted Cloud Data with Privacy Preservation

2019 ◽  
Author(s):  
Fei-Ju Hsieh ◽  
Tai-Lin Chin ◽  
Chin-Ya Huang ◽  
Shan-Hsiang Shen ◽  
Chung-An Shen
Keyword(s):  
Privacy Preservation ◽  
Keyword Search ◽  
Cloud Data

Privacy Preserving Anonymization Schemes-On Transaction Data Publishing

2020 ◽  
Vol 17 (9) ◽  
pp. 4623-4626
Author(s):  
Nisha Nehra ◽  
Suneet Kumar
Keyword(s):  
Privacy Preservation ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Transaction Data ◽  
Past Work ◽  
The Past ◽  
Identity Disclosure ◽  
Comprehensive Survey ◽  
Work Done ◽  
Transactional Data

Now days, due to the sheer amount of data, its complexity and the rate at which it is generated, traditional algorithms that are present so far for the privacy preservation of relation data publishing are not capable enough to ensure privacy as efficiently for transactional data also. From last two decades the interest also increases to provide better data preserving schemes for data publishing. There are a number of algorithms, schemes, models and techniques in the literature that ensure privacy against identity disclosure and attribute disclosure attacks. This paper is a comprehensive survey of the past work done in the field of anonymization to provide privacy against transactional data publishing.

scholarly journals A Quantum-Based Database Query Scheme for Privacy Preservation in Cloud Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Wenjie Liu ◽  
Peipei Gao ◽  
Zhihao Liu ◽  
Hanwu Chen ◽  
Maojun Zhang
Keyword(s):  
Data Privacy ◽  
Privacy Preservation ◽  
Communication Complexity ◽  
Data Item ◽  
Data Service ◽  
Cloud Environment ◽  
Database Query ◽  
Encrypted Data ◽  
Cloud Data ◽  
Management Effort

Cloud computing is a powerful and popular information technology paradigm that enables data service outsourcing and provides higher-level services with minimal management effort. However, it is still a key challenge to protect data privacy when a user accesses the sensitive cloud data. Privacy-preserving database query allows the user to retrieve a data item from the cloud database without revealing the information of the queried data item, meanwhile limiting user’s ability to access other ones. In this study, in order to achieve the privacy preservation and reduce the communication complexity, a quantum-based database query scheme for privacy preservation in cloud environment is developed. Specifically, all the data items of the database are firstly encrypted by different keys for protecting server’s privacy, and in order to guarantee the clients’ privacy, the server is required to transmit all these encrypted data items to the client with the oblivious transfer strategy. Besides, two oracle operations, a modified Grover iteration, and a special offset encryption mechanism are combined together to ensure that the client can correctly query the desirable data item. Finally, performance evaluation is conducted to validate the correctness, privacy, and efficiency of our proposed scheme.

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