Improved Construction for Inner Product Functional Encryption

Functional encryption (FE) is a vast new paradigm for encryption scheme which allows tremendous flexibility in accessing encrypted data. In a FE scheme, a user can learn specific function of encrypted messages by restricted functional key and reveals nothing else about the messages. Besides the standard notion of data privacy in FE, it should protect the privacy of the function itself which is also crucial for practical applications. In this paper, we construct a secret key FE scheme for the inner product functionality using asymmetric bilinear pairing groups of prime order. Compared with the existing similar schemes, our construction reduces both necessary storage and computational complexity by a factor of 2 or more. It achieves simulation-based security, security strength which is higher than that of indistinguishability-based security, against adversaries who get hold of an unbounded number of ciphertext queries and adaptive secret key queries under the External Decisional Linear (XDLIN) assumption in the standard model. In addition, we implement the secret key inner product scheme and compare the performance with the similar schemes.

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Efficient functional encryption for inner product with simulation-based security

Cybersecurity ◽

10.1186/s42400-020-00067-1 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Wenbo Liu ◽

Qiong Huang ◽

Xinjian Chen ◽

Hongbo Li

Keyword(s):

Inner Product ◽

Encryption Scheme ◽

Secret Key ◽

Functional Encryption ◽

Private Key ◽

Simulation Based ◽

Encryption And Decryption ◽

The Standard Model ◽

Secret Keys ◽

Decisional Linear Assumption

AbstractFunctional 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.

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Practical CCA-Secure Functional Encryptions for Deterministic Functions

Security and Communication Networks ◽

10.1155/2020/8823788 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Huige Wang ◽

Kefei Chen ◽

Tianyu Pan ◽

Yunlei Zhao

Keyword(s):

Affirmative Answer ◽

Inner Product ◽

Secret Key ◽

Functional Encryption ◽

Fine Grained ◽

Indistinguishability Obfuscation ◽

Simulation Based ◽

Multilinear Maps ◽

Hard Problems ◽

Generic Transformation

Functional encryption (FE) can implement fine-grained control to encrypted plaintext via permitting users to compute only some specified functions on the encrypted plaintext using private keys with respect to those functions. Recently, many FEs were put forward; nonetheless, most of them cannot resist chosen-ciphertext attacks (CCAs), especially for those in the secret-key settings. This changed with the work, i.e., a generic transformation of public-key functional encryption (PK-FE) from chosen-plaintext (CPA) to chosen-ciphertext (CCA), where the underlying schemes are required to have some special properties such as restricted delegation or verifiability features. However, examples for such underlying schemes with these features have not been found so far. Later, a CCA-secure functional encryption from projective hash functions was proposed, but their scheme only applies to inner product functions. To construct such a scheme, some nontrivial techniques will be needed. Our key contribution in this work is to propose CCA-secure functional encryptions in the PKE and SK environment, respectively. In the existing generic transformation from (adaptively) simulation-based CPA- (SIM-CPA-) secure ones for deterministic functions to (adaptively) simulation-based CCA- (SIM-CCA-) secure ones for randomized functions, whether the schemes were directly applied to CCA settings for deterministic functions is not implied. We give an affirmative answer and derive a SIM-CCA-secure scheme for deterministic functions by making some modifications on it. Again, based on this derived scheme, we also propose an (adaptively) indistinguishable CCA- (IND-CCA-) secure SK-FE for deterministic functions. The final results show that our scheme can be instantiated under both nonstandard assumptions (e.g., hard problems on multilinear maps and indistinguishability obfuscation (IO)) and under standard assumptions (e.g., DDH, RSA, LWE, and LPN).

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Private Predicate Encryption for Inner Product from Key-Homomorphic Pseudorandom Function

Security and Communication Networks ◽

10.1155/2021/6678194 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yi-Fan Tseng ◽

Zi-Yuan Liu ◽

Jen-Chieh Hsu ◽

Raylin Tso

Keyword(s):

Discrete Logarithm ◽

Public Key ◽

Inner Product ◽

Sensitive Information ◽

Public Key Encryption ◽

Secret Key ◽

New Paradigm ◽

Generic Construction ◽

Predicate Encryption ◽

Pseudorandom Function

Predicate encryption (PE), formalized by Katz et al., is a new paradigm of public-key encryption that conceptually captures the public-key encryption that supports fine-grained access control policy. Because of the nature of PE, it is used for cloud storage so that users can retrieve encrypted data without revealing any information about the data to cloud servers and other users. Although lots of PE schemes have been studied, the predicate-hiding security is seldom considered; that is, the user’s secret key may leak sensitive information of the predicate. Additionally, the security of the current predicate-hiding PE schemes relies on the discrete logarithm assumption which cannot resist the quantum attacks in the future. In this paper, we propose a generic PE for inner product under symmetric-key setting, called private IPE, from specific key-homomorphic pseudorandom function (PRF). The rigorous proofs are provided to show that the construction is payload-hiding, attribute-hiding, and predicate-hiding secure. With the advantage of the generic construction, if the underlying PRF can resist quantum attacks, then, through our proposed generic construction, a quantum-resistant private IPE can be obtained.

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An Approach Enabling Various Queries on Encrypted Industrial Data Stream

Security and Communication Networks ◽

10.1155/2019/6293970 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12

Author(s):

Tao Wang ◽

Bo Yang ◽

Guoyong Qiu ◽

Lina Zhang ◽

Yong Yu ◽

...

Keyword(s):

Data Stream ◽

Inner Product ◽

Adaptive Security ◽

Encryption Scheme ◽

Secret Key ◽

Product Evaluations ◽

Functional Encryption ◽

Industrial Data ◽

Encrypted Data ◽

Industrial Internet

Massive data are generated and collected by devices in the industrial Internet of Things. Data sources would encrypt the data and send them to the data center through the gateway. For some supervision purpose, the gateway needs to observe the encrypted data stream and label the suspicious data. Instead of decrypting ciphertext at the gateway, which is not efficient, this paper presents a Φ-searchable functional encryption scheme that supports inner product evaluations on encrypted data. Based on this scheme, an approach enabling various queries on the encrypted industrial data stream is proposed. The adaptive security of our proposed underlying functional encryption scheme can be proven under general subgroup decision assumptions, and our scheme has the smaller public key, the smaller secret key, and the smaller ciphertext size compared to the related schemes. In addition, the experimental results show that our proposed scheme is efficient. Especially for the gateway, querying on the encrypted data only needs less than 20ms, which is practical for industrial data stream auditing scenario.

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