Efficient multi-keyword fuzzy search over encrypted data is a desirable technology for data outsourcing in cloud storage.However,the current searchable encryption solutions still have deficiencies in search efficiency...Efficient multi-keyword fuzzy search over encrypted data is a desirable technology for data outsourcing in cloud storage.However,the current searchable encryption solutions still have deficiencies in search efficiency,accuracy and multiple data owner support.In this paper,we propose an encrypted data searching scheme that can support multiple keywords fuzzy search with order preserving(PMS).First,a new spelling correction algorithm-(Possibility-Levenshtein based Spelling Correction)is proposed to correct user input errors,so that fuzzy keywords input can be supported.Second,Paillier encryption is introduced to calculate encrypted relevance score of multiple keywords for order preserving.Then,a queue-based query method is also applied in this scheme to break the linkability between the query keywords and search results and protect the access pattern.Our proposed scheme achieves fuzzy matching without expanding the index table or sacrificing computational efficiency.The theoretical analysis and experiment results show that our scheme is secure,accurate,error-tolerant and very efficient.展开更多
To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security,retrieval efficiency,and retrieval accuracy.This research suggests a searchable encryption and deep ha...To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security,retrieval efficiency,and retrieval accuracy.This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure,searchable encryption scheme.First,a deep learning framework based on residual network and transfer learn-ing model is designed to extract more representative image deep features.Secondly,the central similarity is used to quantify and construct the deep hash sequence of features.The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index.Finally,according to the additive homomorphic property of Paillier homomorphic encryption,a similarity measurement method suitable for com-puting in the retrieval system’s security is ensured by the encrypted domain.The experimental results,which were obtained on Web Image Database from the National University of Singapore(NUS-WIDE),Microsoft Common Objects in Context(MS COCO),and ImageNet data sets,demonstrate the system’s robust security and precise retrieval,the proposed scheme can achieve efficient image retrieval without revealing user privacy.The retrieval accuracy is improved by at least 37%compared to traditional hashing schemes.At the same time,the retrieval time is saved by at least 9.7%compared to the latest deep hashing schemes.展开更多
Blockchain is a shared database with excellent characteristics,such as high decentralization and traceability.However,data leakage is still a major problem for blockchain transactions.To address this issue,this work i...Blockchain is a shared database with excellent characteristics,such as high decentralization and traceability.However,data leakage is still a major problem for blockchain transactions.To address this issue,this work introduces KPH(Paillier Homomorphic Encryption with Variable k),a privacy protection strategy that updates the transaction amount using the enhanced Paillier semihomomorphic encryption algorithm and verifies the transaction using the FO commitment.Unlike the typical Paillier algorithm,theKPHscheme’s Paillier algorithm includes a variable k and combines the L function and the Chinese remainder theorem to reduce the time complexity of the algorithm from O(|n|2+e)to O(logn),making the decryption process more efficient.展开更多
In recent years,the problem of privacy leakage has attracted increasing attentions.Therefore,machine learning privacy protection becomes crucial research topic.In this paper,the Paillier homomorphic encryption algorit...In recent years,the problem of privacy leakage has attracted increasing attentions.Therefore,machine learning privacy protection becomes crucial research topic.In this paper,the Paillier homomorphic encryption algorithm is proposed to protect the privacy data.The original LeNet-5 convolutional neural network model was first improved.Then the activation function was modified and the C5 layer was removed to reduce the number of model parameters and improve the operation efficiency.Finally,by mapping the operation of each layer in the convolutional neural network from the plaintext domain to the ciphertext domain,an improved LeNet-5 model that can run on encrypted data was constructed.The purpose of using machine learning algorithmwas realized and privacywas ensured at the same time.The analysis shows that the model is feasible and the efficiency is improved.展开更多
基金This work is supported by the National Natural Science Foundation of China under Grant 61402160 and 61872134Hunan Provincial Natural Science Foundation under Grant 2016JJ3043Open Funding for Universities in Hunan Province under grant 14K023.
文摘Efficient multi-keyword fuzzy search over encrypted data is a desirable technology for data outsourcing in cloud storage.However,the current searchable encryption solutions still have deficiencies in search efficiency,accuracy and multiple data owner support.In this paper,we propose an encrypted data searching scheme that can support multiple keywords fuzzy search with order preserving(PMS).First,a new spelling correction algorithm-(Possibility-Levenshtein based Spelling Correction)is proposed to correct user input errors,so that fuzzy keywords input can be supported.Second,Paillier encryption is introduced to calculate encrypted relevance score of multiple keywords for order preserving.Then,a queue-based query method is also applied in this scheme to break the linkability between the query keywords and search results and protect the access pattern.Our proposed scheme achieves fuzzy matching without expanding the index table or sacrificing computational efficiency.The theoretical analysis and experiment results show that our scheme is secure,accurate,error-tolerant and very efficient.
基金supported by the National Natural Science Foundation of China(No.61862041).
文摘To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security,retrieval efficiency,and retrieval accuracy.This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure,searchable encryption scheme.First,a deep learning framework based on residual network and transfer learn-ing model is designed to extract more representative image deep features.Secondly,the central similarity is used to quantify and construct the deep hash sequence of features.The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index.Finally,according to the additive homomorphic property of Paillier homomorphic encryption,a similarity measurement method suitable for com-puting in the retrieval system’s security is ensured by the encrypted domain.The experimental results,which were obtained on Web Image Database from the National University of Singapore(NUS-WIDE),Microsoft Common Objects in Context(MS COCO),and ImageNet data sets,demonstrate the system’s robust security and precise retrieval,the proposed scheme can achieve efficient image retrieval without revealing user privacy.The retrieval accuracy is improved by at least 37%compared to traditional hashing schemes.At the same time,the retrieval time is saved by at least 9.7%compared to the latest deep hashing schemes.
基金funded by the Emerging Interdisciplinary Project of CUFE,the National Natural Science Foundation of China (No.61906220)Ministry of Education of Humanities and Social Science project (No.19YJCZH178).
文摘Blockchain is a shared database with excellent characteristics,such as high decentralization and traceability.However,data leakage is still a major problem for blockchain transactions.To address this issue,this work introduces KPH(Paillier Homomorphic Encryption with Variable k),a privacy protection strategy that updates the transaction amount using the enhanced Paillier semihomomorphic encryption algorithm and verifies the transaction using the FO commitment.Unlike the typical Paillier algorithm,theKPHscheme’s Paillier algorithm includes a variable k and combines the L function and the Chinese remainder theorem to reduce the time complexity of the algorithm from O(|n|2+e)to O(logn),making the decryption process more efficient.
基金The National Natural Science Foundation of China(No.61572521)Engineering University of PAP Innovation Team Science Foundation(No.KYTD201805)Natural Science Basic Research Plan in Shaanxi Province of China(2021JM252).
文摘In recent years,the problem of privacy leakage has attracted increasing attentions.Therefore,machine learning privacy protection becomes crucial research topic.In this paper,the Paillier homomorphic encryption algorithm is proposed to protect the privacy data.The original LeNet-5 convolutional neural network model was first improved.Then the activation function was modified and the C5 layer was removed to reduce the number of model parameters and improve the operation efficiency.Finally,by mapping the operation of each layer in the convolutional neural network from the plaintext domain to the ciphertext domain,an improved LeNet-5 model that can run on encrypted data was constructed.The purpose of using machine learning algorithmwas realized and privacywas ensured at the same time.The analysis shows that the model is feasible and the efficiency is improved.
