An Approach to the Construction of a Recursive Argument of Polynomial Evaluation in the Discrete Log Setting

Succinct Non-interactive Arguments of Knowledge (SNARks) are receiving a lot of attention as a core privacy-enhancing technology for blockchain applications. Polynomial commitment schemes are important building blocks for the construction of SNARks. Polynomial commitment schemes enable the prover to commit to a secret polynomial of the prover and convince the verifier that the evaluation of the committed polynomial is correct at a public point later. Bünz et al. recently presented a novel polynomial commitment scheme with no trusted setup in Eurocrypt’20. To provide a transparent setup, their scheme is built over an ideal class group of imaginary quadratic fields (or briefly, class group). However, cryptographic assumptions on a class group are relatively new and have, thus far, not been well-analyzed. In this paper, we study an approach to transpose Bünz et al.’s techniques in the discrete log setting because the discrete log setting brings a significant improvement in efficiency and security compared to class groups. We show that the transposition to the discrete log setting can be obtained by employing a proof system for the equality of discrete logarithms over multiple bases. Theoretical analysis shows that the transposition preserves security requirements for a polynomial commitment scheme.

A Commitment Scheme with Output Locality-3 Fit for the IoT Device

Low output locality is a property of functions, in which every output bit depends on a small number of input bits. In IoT devices with only a fragile CPU, it is important for many IoT devices to cooperate to execute a single function. In such IoT’s collaborative work, a feature of low output locality is very useful. This is why it is desirable to reconstruct cryptographic primitives with low output locality. However, until now, commitment with a constant low output locality has been constructed by using strong randomness extractors from a nonconstant-output-locality collision-resistant hash function. In this paper, we construct a commitment scheme with output locality-3 from a constant-output-locality collision-resistant hash function for the first time. We prove the computational hiding property of our commitment by the decisional M , δ -bSVP assumption and prove the computational binding property by the M , δ -bSVP assumption, respectively. Furthermore, we prove that the M , δ -bSVP assumption can be reduced to the decisional M , δ -bSVP assumption. We also give a parameter suggestion for our commitment scheme with the 128 bit security.

Improved two factor fuzzy commitment scheme for securing IoT device

Purpose Generally, Internet-of-Things (IoT) is quite small sized with limited resource and low cost that may be vulnerable for physical and cloned attacking. All kind of authentication protocols designed to IoT devices are robust despite which it is prone to attack by hackers. In order to resolve this issue, there are various researches that have introduced the best method for obscuring the cryptographic key. However, the studies have majorly aimed to generate the key dynamically from noise data by Fuzzy Extractor (FE) or Fuzzy Commitment (FC). Hence, these methods have utilized this kind of data with noisy source namely Physical Unclonable Function (PUF) or biometric data. There are several IoT devices that get operated over undermined environment in which biometric data is not available but the technique utilized with biometric data can't be used to undermined IoT devices. Even though, the PUF technique is implemented for the undermined IoT devices this is quite vulnerable over physical attacks inclusive of accidental move and theft. Design/methodology/approach This paper has proposed an advanced scheme in fuzzy commitment over IoT devices which is said to be Improved Two Factor Fuzzy Commitment Scheme (ITFFCS) and this proposed ITFFCS has used two kind of noisy factors present inside and outside the IoT devices. Though, an intruder has accomplished the IoT devices with an access to the internal noisy source, the intruder can't select an exact key from the available data which have been compared using comparable module as an interest. Findings Moreover, the proposed ITFFC method results are compared with existing Static Random Accessible Memory (SRAM) PUF in enterprises application which illustrated the proposed ITFFC method with PUF has accomplished better results in parameters such as energy consumption, area utilization, False Acceptance Ratio (FAR) and Failure Rejection Ratio (FRR). Originality/value Thus, the proposed ITFFCS-PUF is comparatively better than existing method in both FAR and FRR with an average of 0.18% and 0.28%.

An Indistinguishably Secure Function Encryption Scheme

In this work, we first design a function encryption scheme by using key encapsulation. We combine public key encryption with symmetric encryption to implement the idea of key encapsulation. In the key encapsulation, we use a key to turn a message (plaintext) into a ciphertext by symmetric encryption, and then we use public key encryption to turn this key into another ciphertext. In the design of function encryption scheme, we use the public key encryption system, symmetric encryption system, noninteractive proof system, indistinguishable obfuscator, and commitment scheme. Finally, we prove the indistinguishable security of our function encryption scheme.