Privacy Preservation in Data Mining
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Active In SP

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Joined: Apr 2010
10-04-2010, 02:33 PM

In the present times, maintaining privacy of personal information is of great concern. Data mining is a technology that can easily be abused. Privacy preservation becomes an important issue in the development progress of data mining techniques. The project and implimentation involves studying the different aspects and methods of Privacy Preservation in Data Mining and working towards improving the recent approaches developed in the mentioned field.
Active In SP

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Joined: Feb 2010
05-05-2010, 01:10 PM

hey dude can u send me the project and implimentation code n its reoprt 2 the following mail thankku
Active In SP

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Joined: Mar 2012
23-03-2012, 03:49 PM

hey.. anybody having project and implimentation report and running project and implimentation on privacy preserving data mining. if yes, then please mail me on my id-
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29-08-2012, 09:35 PM

Hey bro.. can ypu send me the project and implimentation code of privacy preserving data mining on this mail,
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Joined: Nov 2012
05-12-2012, 12:23 PM

Privacy-Preserving Data Mining

.ppt   privacy.ppt (Size: 379.5 KB / Downloads: 53)

Presentation organization

Associate Data Mining with Privacy
Privacy Preserving Data Mining scheme using random perturbation
Privacy Preserving Data Mining using Randomized Response Techniques
Comparing these two cases

Privacy value

Filtering to weed out unwanted information
Better search results with less effort
Useful recommendations
Market trend …………
From the analysis of a large number of purchase transaction records with the costumers’ age and income, we know what kind of costumers like some style or brand.

Motivation (Introducing Data Mining )

Data Mining’s goal: discover knowledge, trends, patterns from large amounts of data.
Data Mining’s primary task: developing accurate models about aggregated data without access to precise information in individual data records. (Not only discovering knowledge but also preserving privacy)

Presentation organization

Associate Data Mining with Privacy
Privacy Preserving Data Mining scheme using random perturbation
Privacy Preserving Data Mining using Randomized Response Techniques
Comparing these two cases

Privacy preserving methods

Value-class Membership: the values for an attribute are partitioned into a set of disjoint, mutually-exclusive classes.
Value Distortion: xi+r instead of xi where r is a random value.
a) Uniform
b) Gaussian

Reconstruction problem

View the n original data value x1,x2,…xn of a one-dimensional distribution as realizations of n independent identically distributed (iid) random variables X1,X2,…Xn, each with the same distribution as the random variable X.
To hide these data values, n independent random variables Y1,Y2…Yn have been used, each with the same distribution as a different random variable Y.

Randomized Response

A survey contains a sensitive attribute A.
Instead of asking whether the respondent has the attribute A, ask two related questions, the answer to which are opposite to each other (have A no A).
Respondent use a randomizing device to decide which question to answer. The device is designed in such way that the probability of choosing the first question is θ .
study tips
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02-05-2013, 04:04 PM

Privacy-Preserving Data Mining System

.doc   Privacy-Preserving Data.doc (Size: 24 KB / Downloads: 7)


Although successful in many applications, data mining poses special concerns for private data. An integrated architecture takes a systematic view of the problem, implementing established protocols for data collection, inference control, and information sharing.
Privacy-preserving data mining usually has multiple steps that translate to a three-tiered architecture: At the bottom tier are the data Providers, the data owners, which are often physically distributed. The data providers submit their private data to the data warehouse server. This server, which constitutes the middle tier, supports online analytical data processing to facilitate data mining by translating raw data from the data providers into aggregate data the data mining servers can more quickly process.


The architecture typically has three tiers: data providers, which are the data owners;
The data warehouse server, which supports online analytical processing
The Data Mining Server, which perform data mining tasks and share information.
This can be developed by implementing three modules:
1. Data Collection Protocol
2. Inference Control Protocol
3. Information Sharing Protocol

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