The Traveling salesperson problem is one of the problem in mathematics and computer science which haddrown attention as it is easy to understand and difficult to solve. In this paper, we survey the various methods/techniques available to solve traveling salesman problem and analyze it to make critical evaluation of their time complexities. An implementation of the traveling salesman problem using dynamic programming is also presented in this paper which generates optimal answer and tested with 25 cities and it executes in reasonable time.

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In today's era where almost every task is performed through web applications, the need to assure the security of web applications has increased. A survey held in 2010 shows web application vulnerabilities and SQL Injection attack ranked among top five [1]. SQL Injection attack (SQLIA) is performed by those persons who want to access the database and want to steal, change or delete the data which they do not have permission to access [1]. In SQLIA adversary requests through a malicious query which shows some confidential data [2]. In research, it is also proved that when a network and host-level entry point is highly secured, the public interface provided by an application is the one and only source of SQL injection attack. SQLIA can't be applied without using space, single quotes or double dashes [3]. So to prevent SQLIA, these options are taken in observation. Previous model [10] used JDBC-LDAP library which did not support instances, alias and set operations (UNION and UNION ALL). If a query with injection is accepted by any database which is based on relational approach, then it will be accepted by all databases that are based on relational approach. This paper is focused on SQLIA and its techniques and encounters the shortcoming of previous models. This paper proposed a model which uses two databases one relational and other hierarchical to ensure about injection in a query, compare the results by applying tokenization technique on both databases. If the results are same, there is no injection, otherwise it is present. The proposed model uses a tokenization technique so; query containing Alias, Instances and Set operations can also be blocked at the entry point.

Today's advanced scenario where each information is available in one click, data security is the main aspect. Individual information which sometimes needs to be hiding is easily available using some tricks. Medical information, income details are needed to be kept away from adversaries and so, are stored in private tables. Some publicly released information contains zip code, sex, birth date. When this released information is linked with the private table, adversary can detect the whole confidential information of individuals or respondents, i.e. name, medical status. So to protect respondents identity, a new concept k-anonymity is used which means each released record has at least (k-1) other records in the release whose values are distinct over those fields that appear in the external data. K-anonymity can be achieved easily in case of single sensitive attributes i.e. name, salary, medical status, but it is quiet difficult when multiple sensitive attributes are present. Generalization and Suppression are used to achieve k-anonymity. This paper provides a formal introduction of k-anonymity and some techniques used with it l-diversity, t-closeness. This paper covers k-anonymity model and the comparative study of these concepts along with a new proposed concept for multiple sensitive attributes.

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Privacy preserving data mining has two major directions: one is

In this paper, a novel approach has been proposed for hiding sensitive association rules based on the affinity between the frequent items of the transaction. The affinity between the items is defined as Jaccard similarity. This work proposes five algorithms to ensure the minimum side-effects resulting after applying sanitization algorithms to hide sensitive knowledge. Transaction affinity has been introduced which is calculated by adding the affinity of frequent items present in the transaction with the victim-item (item to be modified). Transactions are selected either by increasing or decreasing value of affinity for data distortion to hide

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