Category: Scientific & Academic

QKD – How Quantum Cryptography Key Distribution Works

QKD – Quantum key distribution is the magic part of quantum cryptography. Every other part of this new cryptography mechanism remains the same as in standard cryptography techniques currently used.

By using quantum particles which behave under rules of quantum mechanics, keys can be generated and distributed to receiver side in completely safe way. Quantum mechanics principle, which describes the base rule protecting the exchange of keys, is Heisenberg’s Uncertainty Principle.

Heisenberg’s Uncertainty Principle states that it is impossible to measure both speed and current position of quantum particles at the same time. It furthermore states that the state of observed particle will change if and when measured. This fairly negative axiom which says that measurement couldn’t be done without perturbing the system is used in positive way by quantum key distribution.

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Introduction to Quantum Cryptography

Quantum cryptography is a new technique of securing computer network communication channel. Existing standard crypto systems are using advanced algorithms to create key pairs which are extremely hard to inverse engineer. Quantum cryptography avoids any mathematical algorithm and uses principles of quantum physics.

Quantum crypto implements a new technique of generating and exchanging crypto keys which makes it impossible for third party entities to get those keys by snooping or to create man in the middle by snooping and sending copies of original key. Keys generated in this way will automatically destroy themselves if read by third-party interferer.

When generated between two sides, using quantum key distribution, secret keys will be used with standard and well known symmetric encryption. The key generation process is the only part which uses quantum principles to work, from there, using this “hyper-secure key” already existing symmetric encryption will be used to encrypt and decrypt data, which will be sent over standard, currently available, optic data networks.

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If you want to send or store data and be sure it is safe from being intercepted, you will use Cryptography. Cryptography uses chipper as mathematical virtual lock to make data scrambled so that is not understandable if intercepted by unauthorized third parties.

There are different cryptography techniques, some of them are: encryption, hashing, and steganography.

Cryptography can be differentiated by usage of different key types:

  • Symmetric Key Encryption
  • Asymmetric Key Encryption

Symmetric Key Encryption is sometimes known as Secret Key Cryptography. Main characteristic of this type of cryptography is the same key usage in encryption and decryption of transferred data. Every change in the secret key will make data decryption impossible.

Asymmetric Key Encryption is known as Public Key Cryptography technique. Main characteristic of this type of cryptography is usage of two sets of keys which are generated for the process. One key is public and other is private. Public key encrypts the data. We can only decrypt that data using appropriate private key. The best part of asymmetric cryptography is that is giving us a technique to share encrypted data and enable the receiver to decrypt that data without sending the decryption key across unsecured network.

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Flowing text is a project done as a part of academic work that I am involved with for last few years at the University of Rijeka – Department of Informatics. It’s a short overview at latest achievements in the field of network automation with some lab experiments done to test different paths across the network. The work was presented at 6th International Conference on Information Technologies and Information Society (ITIS2014).

The scope of ITIS events are the applications of IT, particularly in social sciences. The conference also covers a wider range of topics related to IT and computational modeling and analysis, in the context of our Creative Core project “Simulations” and our Research Program “Complex networks”. These include cloud computing, complex systems and complex networks, bioinformatics, graph theory and optimization, statistical analysis, business and industrial processes, logistics, information systems and security.

Okaj, let’s go…


dr. sc. Božidar Kovačić & Valter Popeškić (me)
University of Rijeka – Department of Informatics

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Next project was one of my first networking Science articles. It is a short look at latest achievements from some of the biggest networking scientists today with some short comments from my mentor and me. The work was presented at 19. International scientific conference of International Federation of Communication Associations. International scientific conference “DIT 2012” accepts and publishes scientific and professional papers and the results of interdisciplinary scientific research, whose area of interest is the development of society, education, science and technology.

Okaj, let’s go on…


dr. sc. Božidar Kovačić & Valter Popeškić (me)
University of Rijeka – Department of Informatics
Theme: 3. New ICT technology, media and e-education;

Abstract or simply, intro…

Today’s computer networks will not be able to resolve the tangled problems that emerge from increasingly throughput-demanding services after their need for resources exceed the capabilities of today’s networking technologies. Cognitive networks have the means to resolve this issue incorporating intelligence to the network functions. Introduction to Cognitive network as a concept brings the view into the future of communication, information and learning using modern technology.

A cognitive network is a network consisting of elements that reason and have the ability to learn. In this way they self-adjust according to different unpredictable network conditions in order to optimize data transmission performance. In a cognitive network, judgments are made to meet the requirements of the network as an entire system, rather than the individual network components. The main reason of the emergence of cognitive networks is to achieve the goal of building intelligent self-adjustable networks and in the same time improve the performance. Intelligent self-adjustable networks will be able to use intelligence to determine ideal network operating state for many tunable parameters.

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