Robust Hash Function Research Articles

Implication of Lightweight and Robust Hash Function to Support Key Exchange in Health Sensor Networks

Internet of Things (IoT) applications are critical for the fast delivery of health information/data in different environments. The wireless sensor network (WSN) can be used within IoT applications to collect this information in the electronic-health sector. However, the essential drawback of WSN and health applications is ensuring that patient and healthcare provider data/information is protected. In addition, exchanging keys and joining the network is the first/most important line of defense to protect health information. Amid all this, the previous search has introduced many key exchange protocols but still suffers from security and performance issues for WSNs and user devices. In this research, we propose a new protocol for exchanging keys and joining the network using security algorithms that are Elliptic-curve Diffie–Hellman (ECDH) and QUARK hash (qh). We focused on applying lightweight and high-security techniques to reduce the burden on WSN resources, by adopting a solid methodological approach to support security first and performance second. The security analysis is simulated with the Scyther tool, and the results indicate that our protocol is able to block key exchange attacks known in the existing research. Furthermore, we carried out a comparison with the results of the recent search in terms of performance, our protocol provides better performance results than the results of the existing search.

Audio Fingerprinting Using a Robust Hash Function Based on the MCLT Peak-Pair

In this paper, we propose an audio fingerprinting using robust hash based on the MCLT (Modulated Complex Lapped Transform) peak-pair. In existing methods, the robust audio fingerprinting is not generated if various distortions occurred; time-scaling, pith-shifting and equalization. To solve this problem, we used the spectrum of the MCLT, an adaptive thresholding method for detection of prominent peaks and the novel hash function in the audio fingerprinting. Experimental results show that the proposed method is highly robust in various distorted environments and achieves better identification rates compared to other methods.

An Authentication Protocol Based on Combined RFID-Biometric System

Radio Frequency Identification (RFID) and biometric technologies saw fast evolutions during the last years and which are used in several applications, such as access control. Among important characteristics in the RFID tags, we mention the limitation of resources (memory, energy, …). Our work focuses on the design of a RFID authentication protocol which uses biometric data and which confirms the secrecy, the authentication and the privacy. Our protocol requires a PRNG (Pseud-Random Number Generator), a robust hash function and Biometric hash function. The Biometric hash function is used to optimize and to protect biometric data. For Security analysis of protocol proposed, we will use AVISPA and SPAN tools to verify the authentication and the secrecy.

Robust Image Hashing Based on Random Gabor Filtering and Dithered Lattice Vector Quantization

In this paper, we propose a robust-hash function based on random Gabor filtering and dithered lattice vector quantization (LVQ). In order to enhance the robustness against rotation manipulations, the conventional Gabor filter is adapted to be rotation invariant, and the rotation-invariant filter is randomized to facilitate secure feature extraction. Particularly, a novel dithered-LVQ-based quantization scheme is proposed for robust hashing. The dithered-LVQ-based quantization scheme is well suited for robust hashing with several desirable features, including better tradeoff between robustness and discrimination, higher randomness, and secrecy, which are validated by analytical and experimental results. The performance of the proposed hashing algorithm is evaluated over a test image database under various content-preserving manipulations. The proposed hashing algorithm shows superior robustness and discrimination performance compared with other state-of-the-art algorithms, particularly in the robustness against rotations (of large degrees).

Secure Robust Hash Functions and Their Applications in Non-interactive Communications

A robust hash function allows different parties to extract a consistent key from a common fuzzy source, e.g., an image gone through noisy channels, which can then be used to establish a cryptographic session key among the parties without the need for interactions. These functions are useful in various communication scenarios, where the security notions are different. The authors study these different security notions in this paper and focus on forgery attacks, where the objective of the attack is to compute the extracted key (hash value) of a given message. This paper will examine information-theoretical security against forgery under chosen message attacks. The authors prove that it is not possible due to the entropy of the hash value of a given message can be reduced arbitrarily when sufficient message/hash pairs have been observed. In this regard, the authors give a computationally secure scheme, where it is computationally infeasible to compute the hash value even when its entropy may not be high.

Spatio–Temporal Transform Based Video Hashing

Identification and verification of a video clip via its fingerprint find applications in video browsing, database search and security. For this purpose, the video sequence must be collapsed into a short fingerprint using a robust hash function based on signal processing operations. We propose two robust hash algorithms for video based both on the discrete cosine transform (DCT), one on the classical basis set and the other on a novel randomized basis set (RBT). The robustness and randomness properties of the proposed hash functions are investigated in detail. It is found that these hash functions are resistant to signal processing and transmission impairments, and therefore can be instrumental in building database search, broadcast monitoring and watermarking applications for video. The DCT hash is more robust, but lacks security aspect, as it is easy to find different video clips with the same hash value. The RBT based hash, being secret key based, does not allow this and is more secure at the cost of a slight loss in the receiver operating curves

On the security of the visual hash function

Robust hash functions are central to the security of multimedia content authentication systems. Such functions are sensitive to a key but are robust to many allowed signal processing operations on the underlying content. The robustness of the hash function to changes in the original content implies the existence of a cluster in the feature space around the original contents feature vector, any point within which getting hashed to the same output. The shape and size of the cluster determines the trade-off between the robustness offered and the security of the authentication system based on the robust hash function. The clustering itself is based on a secret key and hence unknown to the attacker. However, we show that the specific clustering arrived at by the robust visual hash function (VHF) may be possible to learn. Given just an input and its hash bits, we show how to construct a statistical model of the hash function, without any knowledge of the secret key used to compute the hash. We also show how to use this model to engineer arbitrary and malicious collisions. Finally, we propose one possible modification to VHF so that constructing a model that mimics its behavior becomes difficult.