19-03-2015, 11:08 PM
Sir I want to do my final year project on publishing search logs.
I tried but stucked in few concepts so kindly send me the project along with source code as soon as possible it's urgent.
Thankyou
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java code for publishing search logs
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Sir I want to do my final year project on publishing search logs.
I tried but stucked in few concepts so kindly send me the project along with source code as soon as possible it's urgent.
Thankyou
29-03-2017, 01:10 PM
Search engine companies collect the "intent database", the stories of your users' search queries. These search records are a goldmine for researchers. Search engine companies, however, are wary of posting search records so they do not disclose sensitive information.
In this article we analyze algorithms to publish frequent keywords, queries and clicks of a search registry. Firstly it is shown how the methods
Achieving k-anonymity variants are vulnerable to active attacks. We then prove that the strongest guarantee guaranteed by -differential privacy unfortunately provides no utility for this problem. Next, we propose a new ZEALOUS algorithm and show how to set its parameters to achieve (_, δ) -probabilistic privacy. We also contrast our analysis of ZEALOUS with an analysis that achieves (__, δ _) - in distinguishing.
Our work concludes with a large experimental study using real applications where the ZEALOUS and previous work is compared that achieves k-anonymity in the publication of search records. Our results show that ZEALOUS yields a utility comparable to k-anonymity while at the same time achieving much stronger privacy guarantees.
In this article we analyze algorithms to publish frequent keywords, queries and clicks of a search registry. Firstly it is shown how the methods
Achieving k-anonymity variants are vulnerable to active attacks. We then prove that the strongest guarantee guaranteed by -differential privacy unfortunately provides no utility for this problem. Next, we propose a new ZEALOUS algorithm and show how to set its parameters to achieve (_, δ) -probabilistic privacy. We also contrast our analysis of ZEALOUS with an analysis that achieves (__, δ _) - in distinguishing.
Our work concludes with a large experimental study using real applications where the ZEALOUS and previous work is compared that achieves k-anonymity in the publication of search records. Our results show that ZEALOUS yields a utility comparable to k-anonymity while at the same time achieving much stronger privacy guarantees.
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