Comparing Lightweight Hash Functions – PHOTON & Quark - Science Assignment Help

Assignment Task:

Task:

Abstract. In the last few years, much progress has been made in the research field of lightweight hash-functions. Due to the fact, that most conventional hash-functions are too expensive in terms of energy consumption and bad performance, hash-functions have been developed, which are meant to be used on resource-constrained devices. In this paper, we present and compare two promising hash-functions, PHOTON and Quark, in terms of design, security and performance.

Introduction

The Internet of Things (IoT) is one of the most promising research fields. In Germany for example, the government supports the research with the initiative IKT2020. The idea of the IoT is the overall presence of the internet in things of the everyday life. Today, internet connected devices like smart phones, tablets, smart tv’s and multi media applications in cars are quite normal. But the vision of the IoT is the distribution of so called smart objects in every sector of the every day life. An common example is a fridge noticing the missing milk and ordering it via internet. The applications that manage these communication flows are often called IoT applications. In these applications, resource constrained devices (“things” or smart objects) with internet access are used to communicate with each other and servers. The most common examples in the literature are RFID tags and systems-on-chip. The RFID technology was the basis for the identification of objects in processes and therefore has a leading role in the development of the vision IoT

Security features of hash functions

In the design of cryptographic hash functions it is crucial to avoid so called collisions [25]. This means that two input values are mapped to the same hash value. These collisions are used within attacks to the applications of the hash function. Therefore, a hash function should be collision resistant [13]. Two other requirements to hash functions are preimage and second preimage resistance. If a hash function is a one way function, it should meet the features second preimage and preimage resistance. A collision resistant function owns the three properties. Collision resistance: In order to be collision resistant, it must be hard to find any two inputs x and x’ for a certain hash function such that the function outputs the same value: h(x) = h(x 0 ) for x 6= x 0 . Hard means in that case that there is not a more efficient way then just brute forcing. Anyway, because it is not possible to find a message with an equal hash value as a given message, this weakness is not as easy to exploit as weaknesses in the second preimage resistance.

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