Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

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

Security and Communication Networks ◽

10.1155/2018/7254305 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 11

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.

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Privacy Preserving Mining in Code Profiling Data

International Journal of Engineering and Management Research ◽

10.31033/ijemr.8.5.5 ◽

2018 ◽

Vol 8 (5) ◽

Author(s):

Meenakshi Kathayat

Keyword(s):

Data Mining ◽

Software Engineering ◽

Future Development ◽

Data Privacy ◽

Software Metrics ◽

Privacy Preserving ◽

Sensitive Data ◽

Privacy Preserving Data Mining ◽

Encrypted Data

Privacy preserving data mining is an important issue nowadays for data mining. Since various organizations and people are generating sensitive data or information these days. They don’t want to share their sensitive data however that data can be useful for data mining purpose. So, due to privacy preserving mining that data can be mined usefully without harming the privacy of that data. Privacy can be preserved by applying encryption on database which is to be mined because now the data is secure due to encryption. Code profiling is a field in software engineering where we can apply data mining to discover some knowledge so that it will be useful in future development of software. In this work we have applied privacy preserving mining in code profiling data such as software metrics of various codes. Results of data mining on actual and encrypted data are compared for accuracy. We have also analyzed the results of privacy preserving mining in code profiling data and found interesting results.

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

npj Digital Medicine ◽

10.1038/s41746-021-00431-6 ◽

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.

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

Electronics ◽

10.3390/electronics10111367 ◽

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.

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An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree

Security and Communication Networks ◽

10.1155/2021/2313389 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Xiaopeng Yang ◽

Hui Zhu ◽

Songnian Zhang ◽

Rongxing Lu ◽

Xuesong Gao

Keyword(s):

Homomorphic Encryption ◽

Security Analysis ◽

Privacy Preserving ◽

Biometric Identification ◽

Large Dataset ◽

Biometric Data ◽

Communication Costs ◽

Identification Scheme ◽

Biometric Template ◽

Cloud Servers

Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.

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Privacy-Preserving Outsourced Similarity Search

Journal of Database Management ◽

10.4018/jdm.2014070103 ◽

2014 ◽

Vol 25 (3) ◽

pp. 48-71 ◽

Cited By ~ 1

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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A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads

Computers ◽

10.3390/computers9010001 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Yeong-Cherng Hsu ◽

Chih-Hsin Hsueh ◽

Ja-Ling Wu

Keyword(s):

Data Privacy ◽

Nearest Neighbor ◽

Search Algorithm ◽

Data Access ◽

Privacy Preserving ◽

Secret Key ◽

K Nearest Neighbor ◽

Sensitive Data ◽

Cloud Data ◽

Cloud Server

With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method.

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Homomorphic Consortium Blockchain for Smart Home System Sensitive Data Privacy Preserving

IEEE Access ◽

10.1109/access.2019.2916345 ◽

2019 ◽

Vol 7 ◽

pp. 62058-62070 ◽

Cited By ~ 10

Author(s):

Wei She ◽

Zhi-Hao Gu ◽

Xu-Kang Lyu ◽

Qi Liu ◽

Zhao Tian ◽

...

Keyword(s):

Smart Home ◽

Data Privacy ◽

Privacy Preserving ◽

Sensitive Data

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Light Weight Cryptography based Medical Data and Image Encryption Scheme

Webology ◽

10.14704/web/v18i2/web18309 ◽

2021 ◽

Vol 18 (2) ◽

pp. 88-104

Author(s):

M. Raja ◽

Dr.S. Dhanasekaran ◽

Dr.V. Vasudevan

Keyword(s):

Data Privacy ◽

Medical Information ◽

Security Analysis ◽

Encrypted Data ◽

Attribute Based Encryption ◽

Identity Based ◽

Information Confidentiality ◽

Processing And Storage ◽

And Storage ◽

Maintenance Problem

Many medical companies use cloud technology to collect, distribute and transmit medical records. Given the need for medical information, confidentiality is a key issue. In this study, we propose an encrypted scheme based on encrypted data for an electronic healthcare environment. We use hybrid Attribute based encryption and Triple DES encryption technique (ABETDES) scheme, including identity-based cryptography (IBC), to ensure data privacy through communication channels և to improve the reliability of cloud computing. There are also limited indicators of light processing and storage resources. This solves a serious maintenance problem and ensures that a private key is created where it is not blind. The introduction of a security option, a comprehensive security analysis to protect ciphertext, shows that our program is effective against many known attacks and compared to existing methods.

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Privacy-Preserving and Efficient Public Key Encryption with Keyword Search Based on CP-ABE in Cloud

Cryptography ◽

10.3390/cryptography4040028 ◽

2020 ◽

Vol 4 (4) ◽

pp. 28

Author(s):

Yunhong Zhou ◽

Shihui Zheng ◽

Licheng Wang

Keyword(s):

Access Control ◽

Data Privacy ◽

Keyword Search ◽

Good Method ◽

Privacy Preserving ◽

Public Key ◽

Public Key Encryption ◽

Encrypted Data ◽

Fine Grained ◽

Attribute Based Encryption

In the area of searchable encryption, public key encryption with keyword search (PEKS) has been a critically important and promising technique which provides secure search over encrypted data in cloud computing. PEKS can protect user data privacy without affecting the usage of the data stored in the untrusted cloud server environment. However, most of the existing PEKS schemes concentrate on data users’ rich search functionalities, regardless of their search permission. Attribute-based encryption technology is a good method to solve the security issues, which provides fine-grained access control to the encrypted data. In this paper, we propose a privacy-preserving and efficient public key encryption with keyword search scheme by using the ciphertext-policy attribute-based encryption (CP-ABE) technique to support both fine-grained access control and keyword search over encrypted data simultaneously. We formalize the security definition, and prove that our scheme achieves selective indistinguishability security against an adaptive chosen keyword attack. Finally, we present the performance analysis in terms of theoretical analysis and experimental analysis, and demonstrate the efficiency of our scheme.

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