Effective Personalized Privacy Reservation

ABSTRACT:

The k-anonymity privacy requirement for publishing micro data requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain “identifying” attributes) contains at least k records. Recently, severalauthors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least ‘well-represented values for each sensitive attribute. In this paper, we show that l-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. Motivated by these limitations, we propose a new notion of privacy called “closeness.” We first present the base model tcloseness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). t-closeness that offers higher utility. We describe our desiderata for designing a distance measure between two probability distributions and present two distance measures. We discuss the rationale for using closeness as a privacy measure and illustrate its advantages through examples and experiments.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• While k-anonymity protects against identity disclosure, it is insufficient to prevent attribute disclosure.
• l-diversity it may be difficult to achieve and may not provide sufficient privacy protection. But it is also insufficient to prevent attribute disclosure.

DISADVANTAGES OF EXISTING SYSTEM:

• Tree measure.
• Attribute level uncertainty

PROPOSED SYSTEM:

• Motivated by these above limitations, we have proposed a novel privacy notion called “closeness.” We propose two instantiations: a base model called t-closeness and a more flexible privacy model called (n, t) -closeness.
• We explain the rationale of the (n, t)-closeness model and show that it achieves a better balance between privacy and utility.
• The (n, t)-closeness model better protects the data while improving the utility of the released data.

ADVANTAGES OF PROPOSED SYSTEM:

• Good Privacy
• No Data Leakage
• More Secure

MODULES

1. Admin Module
2. Analyst Module
3. Doctor Module
4. Receptionist Module

MODULES DESCRIPTION

1. Admin Module

Admin having the rights to view all patient details without suppression, He can view all employee details, he can allow the new employee registration and also he can search the patient details based on id or other personal details.

Forms:

      Registration Form:

          In this form user can enter his personal details. He can also create his own user id and password. By using this user id and password he can enter into the feedback form.

 Login Form:

Admin uses the user id and password to enter into the admin forms to and do the required tasks.

2. Analyst Module

In analyst login he can view the sensitive attribute with respect to the attribute discloser for research (i.e. disease according to the patient details). The patient details will not be track able by analyst.

Form:

Analyst Form:

          In this form he can view the suppressed age, pin code with respect to the disease.

3. Doctor Module

In doctor login he can view the patient details based on patient id. And he will give the disease, symptoms, medicine based on patient id and also he can view the previous medicine based on that he can give new medicine.

4. Receptionist Module

Receptionist has the responsibility for new patient. Patient has to undergo for entry process, if new patient comes they has to the details and they receptionist will give Id to the patient. If existing patient comes they will make entry based on the patient id.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System             : Pentium Dual Core.
• Hard Disk       : 120 GB.
• Monitor           : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram                  : 1 GB.

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.
• Coding Language : NET,C#.NET
• Tool                         : Visual Studio 2008
• Database                 : SQL SERVER 2005