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1.1 ABOUT HIDING SENSITIVE:
Large repositories of data contain sensitive information which must be protected against unauthorized access. Here, we are going to hide sensitive information in large database in order to reduce modifications in database and reduce unwanted side effects. e.g., nonsensitive rules falsely hidden and spurious rules falsely generated may be produced in the rule hiding process. Here, we investigate confidentiality issues of a broad category of rules, which are called association rules. If the disclosure risk of some of these rules are above a certain privacy threshold, those rules must be characterized as sensitive. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferencing sensitive data, or they may provide business competitors with an advantage.
1.2 BENEFITS OF HIDING SENSITIVE:
The experimental results show that the undesired side effects are avoided by using our approach. The results also report that in most cases, all the sensitive rules are hidden without generating false rules. Moreover, the good scalability of our approach in terms of database size and the influence of the correlation among rules on rule hiding are observed.
CHAPTER 2
SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Although a business might use a set of programs that it knows and trusts, and might train its administrators and Help Desk personnel to support those programs, administrators lose control as soon as users begin running unknown code. The increasing role of the Internet in business puts your network at a greater risk than ever before.
Such user-installed programs can conflict with other installed programs, change critical configuration data, or introduce a virus. Users often cannot make safe or informed choices about what software to run because viruses intentionally conceal the malicious purpose of the program. Also, the problems that are associated with running unknown code can increase support costs substantially because they lead to more system maintenance, more help desk time, and lost user productivity
2.2 LIMITATIONS:
All the items in a sensitive rule do not appear in any other sensitive rule. With this assumption, hiding a sensitive rule will not affect any other sensitive rule and, therefore, hiding them one at a time or all together will not make any difference. Thus, their algorithms hide one rule at a time and decrease the supports or confidences one unit at a time. Since this work aims at hiding all sensitive rules, it cannot avoid the undesired side effects .
2.3 PROPOSED SYSTEM:
Sensitive rules are the major advantage of the proposed system.
We can get the copy of the database without affecting the original database. For this purpose we are using the sensitive rules. For example, in one organization a salesman always giving the good production means remaining staff those who want to get a good profit can use his database from him. If some one made any changes in that original database means we cannot retrieve the original data again.
Because of that purpose we are building the project to get the copy of database using the sensitive rules. It does not affect the original database forever. It provides restricting access to sensitive data and Security to End user.
We remove the assumption and allow the user to select sensitive rules from all strong rules such as Minimum support threshold (MST) and Minimum Confidence Threshold (MCT).