K anonymity for crowdsourcing database In crowdsourcing database, human operators are embedded into the database engine and collaborate with other conventional database operators to process the queries. Each human operator publishes small HITs (Human Intelligent Task) to the crowdsourcing platform, which consists of a set of database records and corresponding questions for human workers.

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6. K-Anonymity for Crowdsourcing Database
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7. Abstract
In crowdsourcing database, human operators are embedded into the
database engine and collaborate with other conventional database
operators to process the queries. Each human operator publishes small HITs
(Human Intelligent Task) to the crowdsourcing platform, which consists of a
set of database records and corresponding questions for human workers.
The human workers complete the HITs and return the results to the
crowdsourcing database for further processing. In practice, published
records in HITs may contain sensitive attributes, probably causing privacy
leakage so that malicious workers could link them with other public
databases to reveal individual private information. Conventional privacy
protection techniques, such as K-Anonymity, can be applied to partially
solve the problem. However, after generalizing the data, the result of
standard K-Anonymity algorithms may render uncontrollable information
loss and affects the accuracy of crowdsourcing. In this paper, we first study
the tradeoff between the privacy and accuracy for the human operator
within data anonymization process

8. Introduction
After the employer submits his job to the crowdsourcing platform,
thousands of registered workers will become his candidate employees to
provide the labor on demand. Similar to the Cloud system, the
crowdsourcing platforms charge their users by the payas- you-go model.
They are now considered as the largest online human resource providers.
The LaaS model in crowdsourcing allows us to exploit the unlimited human
workers to complete the complex jobs, which are hard for the computers.
The idea has been introduced into the design of database systems to
process similarity join, fuzzy search and aggregation. In those systems, new
database operators involving human labors are implemented to utilize the
power of the crowd.

9. EXISTING SYSTEM
The effect of K-Anonymity on the crowdsourcing results. We formulate the
problem using a matrix representation. Each entry of the matrix denotes the
probability that human workers return certain answer on a particular
record. This model enables us to estimate the lower bound and upper
bound of the accuracy for the K-Anonymity results. These bounds provide
overall guidelines on the possible reduction on the utility of K-Anonymity
before crowdsourcing the records.

10. PROPOSED SYSTEM
• we introduce the human operator and formalize the K-anonymity
requirement for the human operator.
• we analyze the effect of anonymity on the results of crowdsourcing. We
present our matrix-based probability model.
• we propose our feedback-based approach, which adaptively partitions the
space based on the crowdsourcing results.
• Evaluates the proposed approach using real dataset and Section 6 reviews
previous work on crowdsourcing and K-anonymity.

11. Advantages
• A probability-based matrix model is introduced to estimate
the lower bound and upper bound of the crowdsourcing
accuracy for the anonymized data.
• The model shows that K-Anonymity approach needs to
solve the trade-off between the privacy and the accuracy.
• Experiments on three different datasets show that our
solution can maintain high accuracy results for the
crowdsourcing jobs.

12. Reference
A. Feng et al. “CrowdDB: Query processing with the VLDB
crowd,” Proc. VLDB, vol. 4, no. 12, pp. 1387–1390, 2011.

13. Hardware Specification
Processor : Any Processor above 500 MHz.
Ram : 128Mb.
Hard Disk : 10 Gb.
Compact Disk : 650 Mb.
Input device : Standard Keyboard and Mouse.
Output device : VGA and High Resolution Monitor.

14. Software Specification
Operating System : Windows Family.
Programming Language : ASP.NET, C#.Net.
Database : SQL Server 2005

15. K-Anonymity for Crowdsourcing Database
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