Security Analysis for Hilbert Curve Based Spatial Data Privacy-Preserving Method

2013 ◽  
Author(s):  
Feng Tian ◽  
Xiaolin Gui ◽  
Pan Yang ◽  
Xuejun Zhang ◽  
Jianwei Yang
Keyword(s):  
Spatial Data ◽  
Data Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Hilbert Curve

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.

scholarly journals Protecting Location Privacy for Outsourced Spatial Data in Cloud Storage

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Feng Tian ◽  
Xiaolin Gui ◽  
Jian An ◽  
Pan Yang ◽  
Jianqiang Zhao ◽  
...  
Keyword(s):  
Spatial Data ◽  
Cloud Storage ◽  
Location Privacy ◽  
Security Analysis ◽  
Transformation Method ◽  
Spatial Transformation ◽  
Hilbert Curve ◽  
Space Filling Curve ◽  
Attack Model ◽  
Transformation Methods

As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry. As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded. In this paper, we propose an index modification method for SHC (SHC∗) and a density-based space filling curve (DSC) to improve the security of SHC; they can partially violate the distance-preserving property of SHC, so as to achieve better security. We formally define theindistinguishabilityand attack model for measuring the privacy disclosure risk of spatial transformation methods. The evaluation results indicate that SHC∗and DSC are more secure than SHC, and DSC achieves the best index generation performance.

scholarly journals Achieve Efficient and Privacy-Preserving Compound Substring Query over Cloud

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fan Yin ◽  
Rongxing Lu ◽  
Yandong Zheng ◽  
Xiaohu Tang
Keyword(s):  
Data Privacy ◽  
Homomorphic Encryption ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Computing Technique ◽  
Searchable Symmetric Encryption ◽  
Single Attribute ◽  
Multiple Attributes ◽  
Pseudorandom Function ◽  
Extensive Performance

The cloud computing technique, which was initially used to mitigate the explosive growth of data, has been required to take both data privacy and users’ query functionality into consideration. Searchable symmetric encryption (SSE) is a popular solution that can support efficient attribute queries over encrypted datasets in the cloud. In particular, some SSE schemes focus on the substring query, which deals with the situation that the user only remembers the substring of the queried attribute. However, all of them just consider substring queries on a single attribute, which cannot be used to achieve compound substring queries on multiple attributes. This paper aims to address this issue by proposing an efficient and privacy-preserving SSE scheme supporting compound substring queries. In specific, we first employ the position heap technique to design a novel tree-based index to support substring queries on a single attribute and employ pseudorandom function (PRF) and fully homomorphic encryption (FHE) techniques to protect its privacy. Then, based on the homomorphism of FHE, we design a filter algorithm to calculate the intersection of search results for different attributes, which can be used to support compound substring queries on multiple attributes. Detailed security analysis shows that our proposed scheme is privacy-preserving. In addition, extensive performance evaluations are also conducted, and the results demonstrate the efficiency of our proposed scheme.

scholarly journals Privacy-Preserving Fingerprint Authentication Using D-H Key Exchange and Secret Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huiyong Wang ◽  
Mingjun Luo ◽  
Yong Ding
Keyword(s):  
Secret Sharing ◽  
Private Information ◽  
Data Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Key Exchange ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Biometric Data ◽  
Diffie Hellman

Biometric based remote authentication has been widely deployed. However, there exist security and privacy issues to be addressed since biometric data includes sensitive information. To alleviate these concerns, we design a privacy-preserving fingerprint authentication technique based on Diffie-Hellman (D-H) key exchange and secret sharing. We employ secret sharing scheme to securely distribute fragments of critical private information around a distributed network or group, which softens the burden of the template storage center (TSC) and the users. To ensure the security of template data, the user’s original fingerprint template is stored in ciphertext format in TSC. Furthermore, the D-H key exchange protocol allows TSC and the user to encrypt the fingerprint template in each query using a random one-time key, so as to protect the user’s data privacy. Security analysis indicates that our scheme enjoys indistinguishability against chosen-plaintext attacks and user anonymity. Through experimental analysis, we demonstrate that our scheme can provide secure and accurate remote fingerprint authentication.

scholarly journals Privacy Preserving Decomposable Mining Association Rules on Distributed Data

2018 ◽  
Vol 7 (3.13) ◽  
pp. 157
Author(s):  
Ahmed M. Khedr ◽  
Zaher AL Aghbari ◽  
Ibrahim Kamel
Keyword(s):  
Association Rules ◽  
Data Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Data Repository ◽  
Distributed Data ◽  
Privacy And Security ◽  
Data Mining Algorithms ◽  
Computational Overhead ◽  
Mining Association Rules

In distributed computing, data sharing is inevitable, however, moving local databases from one site to another should be avoided because of the computational overhead and privacy consideration. Most of the data mining algorithms are designed assuming that data repository is stored locally. This paper presents a scheme and algorithms for mining association rules in geographically distributed data. The proposed scheme preserves data privacy of the different geographical site by passing secure messages between them. The algorithms minimize the communication cost by exchanging statistical summaries of the local databases. We provide a privacy and security analysis that shows the privacy preserving aspects of the proposed algorithms. Moreover, the paper presents extensive simulation experiments to evaluate the efficiency of the proposed scheme.  

scholarly journals EPCT: An Efficient Privacy-Preserving and Collusion-Resisting Top- k Query Processing in WSNs

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qian Zhou ◽  
Hua Dai ◽  
Jianguo Zhou ◽  
Rongqi Qi ◽  
Geng Yang ◽  
...  
Keyword(s):  
Query Processing ◽  
Data Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Experimental Result ◽  
Communication Overhead ◽  
Symmetric Encryption ◽  
Sensory Data ◽  
Two Phases ◽  
Secure Query Processing

Data privacy threat arises during providing top- k query processing in the wireless sensor networks. This article presents an efficient privacy-preserving and collusion-resisting top- k (EPCT) query processing protocol. A minimized candidate encrypted dataset determination model is first designed, which is the foundation of EPCT. The model guides the idea of query processing and guarantees the correctness of the protocol. The symmetric encryption with different private key in each sensor is deployed to protect the privacy of sensory data even a few sensors in the networks have been colluding with adversaries. Based on the above model and security setting, two phases of interactions between the interested sensors and the sink are designed to implement the secure query processing protocol. The security analysis shows that the proposed protocol is capable of providing secure top- k queries in the manner of privacy protection and anticollusion, whereas the experimental result indicates that the protocol outperforms the existing works on communication overhead.

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 A Secure Ciphertext Retrieval Scheme against Insider KGAs for Mobile Devices in Cloud Storage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Run Xie ◽  
Chanlian He ◽  
Dongqing Xie ◽  
Chongzhi Gao ◽  
Xiaojun Zhang
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Privacy ◽  
Security Analysis ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Security Issues ◽  
Efficiency Comparison ◽  
Cloud Servers

With the advent of cloud computing, data privacy has become one of critical security issues and attracted much attention as more and more mobile devices are relying on the services in cloud. To protect data privacy, users usually encrypt their sensitive data before uploading to cloud servers, which renders the data utilization to be difficult. The ciphertext retrieval is able to realize utilization over encrypted data and searchable public key encryption is an effective way in the construction of encrypted data retrieval. However, the previous related works have not paid much attention to the design of ciphertext retrieval schemes that are secure against inside keyword-guessing attacks (KGAs). In this paper, we first construct a new architecture to resist inside KGAs. Moreover we present an efficient ciphertext retrieval instance with a designated tester (dCRKS) based on the architecture. This instance is secure under the inside KGAs. Finally, security analysis and efficiency comparison show that the proposal is effective for the retrieval of encrypted data in cloud computing.

scholarly journals Study of privacy-preserving framework for cloud storage

2011 ◽  
Vol 8 (3) ◽  
pp. 801-819 ◽  
Author(s):  
Huang Ruwei ◽  
Gui Xiaolin ◽  
Yu Si ◽  
Zhuang Wei
Keyword(s):  
Data Storage ◽  
Cloud Storage ◽  
Security Analysis ◽  
Bloom Filter ◽  
Data Access ◽  
Data Retrieval ◽  
Privacy Preserving ◽  
Index Structure ◽  
Structure Generation ◽  
Access Right

In order to implement privacy-preserving, efficient and secure data storage and access environment of cloud storage, the following problems must be considered: data index structure, generation and management of keys, data retrieval, treatments of change of users? access right and dynamic operations on data, and interactions among participants. To solve those problems, the interactive protocol among participants is introduced, an extirpation-based key derivation algorithm (EKDA) is designed to manage the keys, a double hashed and weighted Bloom Filter (DWBF) is proposed to retrieve the encrypted keywords, which are combined with lazy revocation, multi-tree structure, asymmetric and symmetric encryptions, which form a privacypreserving, efficient and secure framework for cloud storage. The experiment and security analysis show that EKDA can reduce the communication and storage overheads efficiently, DWBF supports ciphertext retrieval and can reduce communication, storage and computation overhead as well, and the proposed framework is privacy preserving while supporting data access efficiently.

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