Performance Analysis Of Secured Synchronous Stream Ciphers

The new information and communication technologies require adequate security. In the past decades ,we have witnessed an explosive growth of the digital storage and communication of data ,triggered by some important breakthroughs such as the Internet and the expansive growth of wireless communications. In the world of cryptography ,stream ciphers are known as primitives used to ensure privacy over communication channel and these are widely used for fast encryption of sensitive data. Lots of old stream ciphers that have been formerly used no longer be considered secure ,because of their vulnerability to newly developed cryptanalysis techniques. Many designs stream ciphers have been proposed in an effort to find a proper candidate to be chosen as world standard for data encryption. From these designs, the stream ciphers which are Trivium,Edon80 and Mickey are implemented in ‘c’ language with out affecting their security .Actually these algorithms are particularly suited for hardware oriented environments which provides considerable security and efficiency aspects. We will be targeting hardware applications, and good measure for efficiency of a stream cipher in this environment is the number of key stream bits generated per cycle per gate. For good efficiency we are approaching two ways .One approach is minimizing the number of gates.The other approach is to dramatically increase the number of bits for cycle. This allows reducing the clock frequency at the cost of an increased gate count. Apart from the implementation the analysis which includes the security of these algorithms against some attacks related to stream ciphers such as guess and deterministic attacks, correlation attacks, divide and conquer attacks and algebraic attacks are presented

Selection of Optimal Discount of Retail Assortments with Data Mining Approach

Recently, the capabilities of generating and collecting data have been increasing rapidly. Widespread use of bar codes for most commercial products, the computerization of many business, and the advance in data collection tools have provided us with huge amount of retail data. This explosive growth in data and databases has generated an urgent need for data mining techniques and tools that can extract implicit, previously unknown and potentially useful information from data in data storages. One of the most popular data mining approaches is association rules , which is commonly applied to analyze market baskets to help managers to determine which items are frequently purchased together by customers. Affinity analysis is a data analysis and data mining technique that discovers co-occurrence relationships among activities performed by (or recorded about) specific individuals or groups. In general, this can be applied to any process where agents can be uniquely identified and information about their activities can be recorded. In retail, affinity analysis is used to perform market basket analysis, in which retailers seek to understand the purchase behavior of customers. This information can then be used for purposes of cross-selling and up-selling, in addition to influencing sales promotions, loyalty programs, store design, and discount plans