Lattice-Based Searchable Encryption Scheme against Inside Keywords Guessing Attack

To save the local storage,users store the data on the cloud server who offers convenient internet services.To guarantee the data privacy,users encrypt the data before uploading them into the cloud server.Since encryption can reduce the data availability,public-key encryption with keyword search(PEKS)is developed to achieve the retrieval of the encrypted data without decrypting them.However,most PEKS schemes cannot resist quantum computing attack,because the corresponding hardness assumptions are some number theory problems that can be solved efficiently under quantum computers.Besides,the traditional PEKS schemes have an inherent security issue that they cannot resist inside keywords guessing attack(KGA).In this attack,a malicious server can guess the keywords encapsulated in the search token by computing the ciphertext of keywords exhaustively and performing the test between the token and the ciphertext of keywords.In the paper,we propose a lattice-based PEKS scheme that can resist quantum computing attacks.To resist inside KGA,this scheme adopts a lattice-based signature technique into the encryption of keywords to prevent the malicious server from forging a valid ciphertext.Finally,some simulation experiments are conducted to demonstrate the performance of the proposed scheme and some comparison results are further shown with respect to other searchable schemes.

Verifiable Identity-Based Encryption with Keyword Search for IoT from Lattice

Internet of Things(IoT),which provides the solution of connecting things and devices,has increasingly developed as vital tools to realize intelligent life.Generally,source-limited IoT sensors outsource their data to the cloud,which arises the concerns that the transmission of IoT data is happening without appropriate consideration of the profound security challenges involved.Though encryption technology can guarantee the confidentiality of private data,it hinders the usability of data.Searchable encryption(SE)has been proposed to achieve secure data sharing and searching.However,most of existing SE schemes are designed under conventional hardness assumptions and may be vulnerable to the adversary with quantum computers.Moreover,the untrusted cloud server may perform an unfaithful search execution.To address these problems,in this paper,we propose the first verifiable identity-based keyword search(VIBKS)scheme from lattice.In particular,a lattice-based delegation algorithm is adopted to help the data user to verify both the correctness and the integrity of the search results.Besides,in order to reduce the communication overhead,we refer to the identity-based mechanism.We conduct rigorous proof to demonstrate that the proposed VIBKS scheme is ciphertext indistinguishable secure against the semi-honestbut-curious adversary.In addition,we give the detailed computation and communication complexity of our VIBKS and conduct a series of experiments to validate its efficiency performance.

IoT Services:Realizing Private Real-Time Detection via Authenticated Conjunctive Searchable Encryption

With the rapid development of wireless communication technology,the Internet of Things is playing an increasingly important role in our everyday.The amount of data generated by sensor devices is increasing as a large number of connectable devices are deployed in many fields,including the medical,agricultural,and industrial areas.Uploading data to the cloud solves the problem of data overhead but results in privacy issues.Therefore,the question of how to manage the privacy of uploading data and make it available to be interconnected between devices is a crucial issue.In this paper,we propose a scheme that supports real-time authentication with conjunctive keyword detection(RA-CKD),this scheme can realize the interconnection of encrypted data between devices while ensuring some measure of privacy for both encrypted data and detection tokens.Through authentication technology,connected devices can both authenticate each other’s identity and prevent malicious adversaries from interfering with device interconnection.Finally,we prove that our scheme can resist inside keyword guessing attack through rigorous security reduction.The experiment shows that the efficiency of RA-CKD is good enough to be practical.

Verifiable Privacy-Preserving Neural Network on Encrypted Data

The widespread acceptance of machine learning,particularly of neural networks leads to great success in many areas,such as recommender systems,medical predictions,and recognition.It is becoming possible for any individual with a personal electronic device and Internet access to complete complex machine learning tasks using cloud servers.However,it must be taken into consideration that the data from clients may be exposed to cloud servers.Recent work to preserve data confidentiality has allowed for the outsourcing of services using homomorphic encryption schemes.But these architectures are based on honest but curious cloud servers,which are unable to tell whether cloud servers have completed the computation delegated to the cloud server.This paper proposes a verifiable neural network framework which focuses on solving the problem of data confidentiality and training integrity in machine learning.Specifically,we first leverage homomorphic encryption and extended diagonal packing method to realize a privacy-preserving neural network model efficiently,it enables the user training over encrypted data,thereby protecting the user’s private data.Then,considering the problem that malicious cloud servers are likely to return a wrong result for saving cost,we also integrate a training validation modular Proof-of-Learning,a strategy for verifying the correctness of computations performed during training.Moreover,we introduce practical byzantine fault tolerance to complete the verification progress without a verifiable center.Finally,we conduct a series of experiments to evaluate the performance of the proposed framework,the results show that our construction supports the verifiable training of PPNN based on HE without introducing much computational cost.

Authorized Attribute-Based Encryption Multi-Keywords Search with Policy Updating

Attribute-based encryption is cryptographic techniques that provide flexible data access control to encrypted data content in cloud storage.Each trusted authority needs proper management and distribution of secret keys to the user’s to only authorized user’s attributes.However existing schemes cannot be applied multiple authority that supports only a single keywords search compare to multi keywords search high computational burden or inefficient attribute’s revocation.In this paper,a ciphertext policy attribute-based encryption(CP-ABE)scheme has been proposed which focuses on multi-keyword search and attribute revocation by new policy updating feathers under multiple authorities and central authority.The data owner encrypts the keywords index under the initial access policy.Moreover,this paper addresses further issues such as data access,search policy,and confidentiality against unauthorized users.Finally,we provide the correctness analysis,performance analysis and security proof for chosen keywords attack and search trapdoor in general group model using DBDH and DLIN assumption.

Searchable Encryption with Access Control on Keywords in Multi-User Setting

Searchable encryption technology makes it convenient to search encrypted data with keywords for people.A data owner shared his data with other users on the cloud server.For security,it is necessary for him to build a fine-grained and flexible access control mechanism.The main idea of this paper is to let the owner classify his data and then authorizes others according to categories.The cloud server maintains a permission matrix,which will be used to verify whether a trapdoor is valid or not.In this way we can achieve access control and narrow the search range at the same time.We prove that our scheme can achieve index and trapdoor indistinguishability under chosen keywords attack security in the random oracles.