scholarly journals Achieving Efficient Conjunctive Keyword Searches over Encrypted Data

2005 ◽  
pp. 414-426 ◽  
Author(s):  
Lucas Ballard ◽  
Seny Kamara ◽  
Fabian Monrose
Keyword(s):  
Encrypted Data ◽  
Keyword Searches

Authorized Keyword Searches on Public Key Encrypted Data With Time Controlled Keyword Privacy

2020 ◽  
Vol 15 ◽  
pp. 2096-2109
Author(s):  
Lingling Xu ◽  
Wanhua Li ◽  
Fangguo Zhang ◽  
Rong Cheng ◽  
Shaohua Tang
Keyword(s):  
Public Key ◽  
Encrypted Data ◽  
Keyword Searches

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

An Efficient Privacy-Preserving Rank Query over Encrypted Data in Cloud Computing

2012 ◽  
Vol 35 (11) ◽  
pp. 2215 ◽  
Author(s):  
Fang-Quan CHENG ◽  
Zhi-Yong PENG ◽  
Wei SONG ◽  
Shu-Lin WANG ◽  
Yi-Hui CUI
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Encrypted Data

Research and design of querying approach for XML encrypted data

2010 ◽  
Vol 30 (4) ◽  
pp. 1099-1102
Author(s):  
Yu-yi KE ◽  
Shi-xiong XIA ◽  
Chu-jiao WANG
Keyword(s):  
Encrypted Data ◽  
Research And Design

A New User-controlled and Efficient Encrypted Data Sharing Model in Cloud Storage

2019 ◽  
Vol 13 (4) ◽  
pp. 356-363
Author(s):  
Yuezhong Wu ◽  
Wei Chen ◽  
Shuhong Chen ◽  
Guojun Wang ◽  
Changyun Li
Keyword(s):  
Data Sharing ◽  
Data Security ◽  
Cloud Storage ◽  
Screening Program ◽  
Original Data ◽  
Third Party ◽  
Active System ◽  
Encrypted Data ◽  
Active User ◽  
User Data

Background: Cloud storage is generally used to provide on-demand services with sufficient scalability in an efficient network environment, and various encryption algorithms are typically applied to protect the data in the cloud. However, it is non-trivial to obtain the original data after encryption and efficient methods are needed to access the original data. Methods: In this paper, we propose a new user-controlled and efficient encrypted data sharing model in cloud storage. It preprocesses user data to ensure the confidentiality and integrity based on triple encryption scheme of CP-ABE ciphertext access control mechanism and integrity verification. Moreover, it adopts secondary screening program to achieve efficient ciphertext retrieval by using distributed Lucene technology and fine-grained decision tree. In this way, when a trustworthy third party is introduced, the security and reliability of data sharing can be guaranteed. To provide data security and efficient retrieval, we also combine active user with active system. Results: Experimental results show that the proposed model can ensure data security in cloud storage services platform as well as enhance the operational performance of data sharing. Conclusion: The proposed security sharing mechanism works well in an actual cloud storage environment.

scholarly journals Cryptosystem Conversion, Packing and Matrix Processing of Homomorphically Encrypted Data: Application to IOT Devices

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 28302-28316
Author(s):  
Maxime Pistono ◽  
Reda Bellafqira ◽  
Gouenou Coatrieux
Keyword(s):  
Encrypted Data ◽  
Data Application ◽  
Iot Devices

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 Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1367
Author(s):  
Raghida El El Saj ◽  
Ehsan Sedgh Sedgh Gooya ◽  
Ayman Alfalou ◽  
Mohamad Khalil
Keyword(s):  
Neural Networks ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Encrypted Data ◽  
High Classification Accuracy ◽  
Hide Information ◽  
Network Methods ◽  
Cloud Environments

Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.

Sign in / Sign up

Export Citation Format

Share Document