Data Processing for Disease-Treatment Relations.
Data Processing for Disease-Treatment Relations
ABSTRACT:
Search engine companies collect the “database of intentions”, the histories of their users’ search queries. These search logs are a gold mine for researchers. Search engine companies, however, are wary of publishing search logs in order not to disclose sensitive information. In this paper we analyze algorithms for publishing frequent keywords, queries and clicks of a search log. We first show how methods that achieve variants of k-anonymity are vulnerable to active attacks. We then demonstrate that the stronger guarantee ensured by differential privacy unfortunately does not provide any utility for this problem. Our paper concludes with a large experimental study using real applications where we compare ZEALOUS and previous work that achieves k-anonymity in search log publishing. Our results show that ZEALOUS yields comparable utility to k−anonymity while at the same time achieving much stronger privacy guarantees.
PROJECT OUTPUT VIDEO:
EXISTING SYSTEM:
Today’s search engines do not just collect and index WebPages, they also collect and mine information about their users. They store the queries, clicks, IP-addresses, and other information about the interactions with users in what is called a search log. Search logs contain valuable information that search engines use to tailor their services better to their users’ needs. They enable the discovery of trends, patterns, and anomalies in the search behavior of users, and they can be used in the development and testing of new algorithms to improve search performance and quality.
DISADVANTAGES OF EXISTING SYSTEM:
Existing work on publishing logs make Scientists all around the world to tap this gold mine for their own research.
The log contain sensitive information about the users, for example searches for diseases, lifestyle choices, personal tastes, and political affiliations.
PROPOSED SYSTEM:
The main focus of this paper is search logs, our results apply to other scenarios as well. For example, consider a retailer who collects customer transactions. Each transaction consists of a basket of products together with their prices, and a time-stamp. In this case ZEALOUS can be applied to publish frequently purchased products or sets of products. This information can also be used in a recommender system or in a market basket analysis to decide on the goods and promotions in a store.
Our evaluation includes applications that use search logs for improving both search experience and search performance, and our results show that ZEALOUS’ output is sufficient for these applications while achieving strong formal privacy guarantees.
ADVANTAGES OF PROPOSED SYSTEM:
Our results show that ZEALOUS yields comparable utility to k−anonymity while at the same time achieving much stronger privacy guarantees.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium i3 Processor
- Hard Disk : 500 GB..
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- RAM : 4 GB.
SOFTWARE REQUIREMENTS:
- Operating system : Windows 10/11.
- Coding Language : C#.net.
- Frontend : ASP.Net, HTML, CSS, JavaScript.
- IDE Tool : VISUAL STUDIO.
- Database : SQL SERVER 2005.
