MAVP-FE:Multi-Authority Vector Policy Functional Encryption with Efficient Encryption and Decryption

In cloud,data access control is a crucial way to ensure data security.Functional encryption(FE) is a novel cryptographic primitive supporting fine-grained access control of encrypted data in cloud.In FE,every ciphertext is specified with an access policy,a decryptor can access the data if and only if his secret key matches with the access policy.However,the FE cannot be directly applied to construct access control scheme due to the exposure of the access policy which may contain sensitive information.In this paper,we deal with the policy privacy issue and present a mechanism named multi-authority vector policy(MAVP) which provides hidden and expressive access policy for FE.Firstly,each access policy is encoded as a matrix and decryptors can only obtain the matched result from the matrix in MAVP.Then,we design a novel function encryption scheme based on the multi-authority spatial policy(MAVPFE),which can support privacy-preserving yet non-monotone access policy.Moreover,we greatly improve the efficiency of encryption and decryption in MAVP-FE by shifting the major computation of clients to the outsourced server.Finally,the security and performance analysis show that our MAVP-FE is secure and efficient in practice.

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data.However,the training mechanism for passing model parameters is still threatened by gradient inversion,inference attacks,etc.With a lightweight encryption overhead,function encryption is a viable secure aggregation technique in federation learning,which is often used in combination with differential privacy.The function encryption in federal learning still has the following problems:a)Traditional function encryption usually requires a trust third party(TTP)to assign the keys.If a TTP colludes with a server,the security aggregation mechanism can be compromised.b)When using differential privacy in combination with function encryption,the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible.In this paper,we propose a hybrid privacy-preserving scheme for federated learning,called Fed-DFE.Specifically,we present a decentralized multi-client function encryption algorithm.It replaces the TTP in traditional function encryption with an interactive key generation algorithm,avoiding the problem of collusion.Then,an embedded incentive mechanism is designed for function encryption.It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy.Subsequently,we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm.The experimental results demonstrate the effectiveness and efficiency of our scheme.

Efficient functional encryption for inner product with simulation-based security

Functional encryption(FE)is a novel paradigm for encryption scheme which allows tremendous flexibility in accessing encrypted information.In FE,a user can learn specific function of encrypted messages by restricted functional key and reveal nothing else about the messages.Inner product encryption(IPE)is a special type of functional encryption where the decryption algorithm,given a ciphertext related to a vector x and a secret key related to a vector y,computes the inner product x·y.In this paper,we construct an efficient private-key functional encryption(FE)for inner product with simulation-based security,which is much stronger than indistinguishability-based security,under the External Decisional Linear assumption in the standard model.Compared with the existing schemes,our construction is faster in encryption and decryption,and the master secret key,secret keys and ciphertexts are shorter.

Efficient functional encryption for inner product with simulation-based security

Functional encryption(FE)is a novel paradigm for encryption scheme which allows tremendous flexibility in accessing encrypted information.In FE,a user can learn specific function of encrypted messages by restricted functional key and reveal nothing else about the messages.Inner product encryption(IPE)is a special type of functional encryption where the decryption algorithm,given a ciphertext related to a vector x and a secret key related to a vector y,computes the inner product x·y.In this paper,we construct an efficient private-key functional encryption(FE)for inner product with simulation-based security,which is much stronger than indistinguishability-based security,under the External Decisional Linear assumption in the standard model.Compared with the existing schemes,our construction is faster in encryption and decryption,and the master secret key,secret keys and ciphertexts are shorter.

Inner product encryption from ring learning with errors

The functional encryption scheme designed using the lattice can realize fine-grained encryption and it can resist quantum attacks.Unfortunately,the sizes of the keys and ciphertexts in cryptographic applications based on learning with errors are large,which makes the algorithm inefficient.Therefore,we construct a functional encryption for inner product predicates scheme by improving the learning with errors scheme of Agrawal et al.[Asiacrypt 2011],and its security relies on the difficulty assumption of ring learning with errors.Our construction can reduce the sizes of the keys and ciphertexts compared with the learning with errors scheme.

Inner product encryption from ring learning with errors

The functional encryption scheme designed using the lattice can realize fine-grained encryption and it can resist quantum attacks.Unfortunately,the sizes of the keys and ciphertexts in cryptographic applications based on learning with errors are large,which makes the algorithm inefficient.Therefore,we construct a functional encryption for inner product predicates scheme by improving the learning with errors scheme of Agrawal et al.[Asiacrypt 2011],and its security relies on the difficulty assumption of ring learning with errors.Our construction can reduce the sizes of the keys and ciphertexts compared with the learning with errors scheme.

Chaos block cipher for wireless sensor network

New block cipher algorithm in single byte for wireless sensor network with excellence of many cipher algorithms is studied. The child keys are generated through the developed discrete Logistic mapping, and the Feistel encrypting function with discrete chaos operation is constructed. The single byte block is encrypted and decrypted through one turn permutation, being divided into two semi-byte, quadri- Feistel structural operation, and one turn permutation again. The amount of keys may be variable with the turns of Feistel structural operation. The random and security of the child key was proven, and the experiment for the block cipher in wireless sensor network was completed. The result indicates that the algorithm is more secure and the chaos block cipher in single byte is feasible for wireless sensor network.