Secure Query Processing over Encrypted Data Using a Distributed Index Structure for Outsourcing Sensitive Data

2019 ◽  
pp. 187-198
Author(s):  
Hyunjo Lee ◽  
Hyeonguk Ma ◽  
Youngho Song ◽  
Jae-Woo Chang
Keyword(s):  
Query Processing ◽  
Index Structure ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Distributed Index ◽  
Secure Query Processing

Energy and Latency Efficient Access of Wireless XML Stream

2010 ◽  
Vol 21 (1) ◽  
pp. 58-79 ◽  
Author(s):  
Jun Pyo Park ◽  
Chang-Sup Park ◽  
Yon Dohn Chung
Keyword(s):  
Query Processing ◽  
Index Structure ◽  
Access Time ◽  
Xml Data ◽  
Mobile Client ◽  
Wireless Broadcast ◽  
Efficient Access ◽  
Distributed Index ◽  
Mobile Clients ◽  
Text Content

In this article, we address the problem of delayed query processing raised by tree-based index structures in wireless broadcast environments, which increases the access time of mobile clients. We propose a novel distributed index structure and a clustering strategy for streaming XML data that enables energy and latencyefficient broadcasting of XML data. We first define the DIX node structure to implement a fully distributed index structure which contains the tag name, attributes, and text content of an element, as well as its corresponding indices. By exploiting the index information in the DIX node stream, a mobile client can access the stream with shorter latency. We also suggest a method of clustering DIX nodes in the stream, which can further enhance the performance of query processing in the mobile clients. Through extensive experiments, we demonstrate that our approach is effective for wireless broadcasting of XML data and outperforms the previous methods.

Energy and Latency Efficient Access of Wireless XML Stream

2012 ◽  
pp. 57-79
Author(s):  
Jun Pyo Park ◽  
Chang-Sup Park ◽  
Yon Dohn Chung
Keyword(s):  
Query Processing ◽  
Index Structure ◽  
Access Time ◽  
Xml Data ◽  
Mobile Client ◽  
Wireless Broadcast ◽  
Efficient Access ◽  
Distributed Index ◽  
Mobile Clients ◽  
Text Content

In this article, we address the problem of delayed query processing raised by tree-based index structures in wireless broadcast environments, which increases the access time of mobile clients. We propose a novel distributed index structure and a clustering strategy for streaming XML data that enables energy and latency-efficient broadcasting of XML data. We first define the DIX node structure to implement a fully distributed index structure which contains the tag name, attributes, and text content of an element, as well as its corresponding indices. By exploiting the index information in the DIX node stream, a mobile client can access the stream with shorter latency. We also suggest a method of clustering DIX nodes in the stream, which can further enhance the performance of query processing in the mobile clients. Through extensive experiments, we demonstrate that our approach is effective for wireless broadcasting of XML data and outperforms the previous methods.

An Efficient, Secure, and Queryable Encryption for NoSQL-Based Databases Hosted on Untrusted Cloud Environments

2019 ◽  
Vol 13 (2) ◽  
pp. 14-31
Author(s):  
Mamdouh Alenezi ◽  
Muhammad Usama ◽  
Khaled Almustafa ◽  
Waheed Iqbal ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Query Processing ◽  
State Of The Art ◽  
Data Communication ◽  
Nosql Databases ◽  
High Concern ◽  
Cloud Environments ◽  
High Scalability ◽  
And Performance ◽  
Secure Query Processing ◽  
Security Concern

NoSQL-based databases are attractive to store and manage big data mainly due to high scalability and data modeling flexibility. However, security in NoSQL-based databases is weak which raises concerns for users. Specifically, security of data at rest is a high concern for the users deployed their NoSQL-based solutions on the cloud because unauthorized access to the servers will expose the data easily. There have been some efforts to enable encryption for data at rest for NoSQL databases. However, existing solutions do not support secure query processing, and data communication over the Internet and performance of the proposed solutions are also not good. In this article, the authors address NoSQL data at rest security concern by introducing a system which is capable to dynamically encrypt/decrypt data, support secure query processing, and seamlessly integrate with any NoSQL- based database. The proposed solution is based on a combination of chaotic encryption and Order Preserving Encryption (OPE). The experimental evaluation showed excellent results when integrated the solution with MongoDB and compared with the state-of-the-art existing work.

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.

Computing Blindfolded on Data Homomorphically Encrypted under Multiple Keys: A Survey

2022 ◽  
Vol 54 (9) ◽  
pp. 1-37
Author(s):  
Asma Aloufi ◽  
Peizhao Hu ◽  
Yongsoo Song ◽  
Kristin Lauter
Keyword(s):  
Homomorphic Encryption ◽  
Lessons Learned ◽  
Secret Key ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Outsourced Computation ◽  
Secret Keys ◽  
Comprehensive Survey ◽  
Multiple Keys ◽  
Cryptographic Techniques

With capability of performing computations on encrypted data without needing the secret key, homomorphic encryption (HE) is a promising cryptographic technique that makes outsourced computations secure and privacy-preserving. A decade after Gentry’s breakthrough discovery of how we might support arbitrary computations on encrypted data, many studies followed and improved various aspects of HE, such as faster bootstrapping and ciphertext packing. However, the topic of how to support secure computations on ciphertexts encrypted under multiple keys does not receive enough attention. This capability is crucial in many application scenarios where data owners want to engage in joint computations and are preferred to protect their sensitive data under their own secret keys. Enabling this capability is a non-trivial task. In this article, we present a comprehensive survey of the state-of-the-art multi-key techniques and schemes that target different systems and threat models. In particular, we review recent constructions based on Threshold Homomorphic Encryption (ThHE) and Multi-Key Homomorphic Encryption (MKHE). We analyze these cryptographic techniques and schemes based on a new secure outsourced computation model and examine their complexities. We share lessons learned and draw observations for designing better schemes with reduced overheads.

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.

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