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  1. Proceedings of the 2010 ACM workshop on Cloud computing security workshop (CCSW '10)
  2. HengHa: data harvesting detection on hidden databases
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Authentication in the clouds: a framework and its application to mobile users
Venus: verification for untrusted cloud storage
Privacy preserving EHR system using attribute-based infrastructure
A new form of DOS attack in a cloud and its avoidance mechanism
First principles vulnerability assessment
Content oriented virtual domains for secure information sharing across organizations
Remote data checking for network coding-based distributed storage systems
Privacy preserving mapping schemes supporting comparison
Towards incident handling in the cloud: challenges and approaches
Security audits of multi-tier virtual infrastructures in public infrastructure clouds
Towards a discipline of mission-aware cloud computing
Seeding clouds with trust anchors
HengHa: data harvesting detection on hidden databases
Determinating timing channels in compute clouds

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HengHa: data harvesting detection on hidden databases

Content Provider ACM Digital Library
Author Wang, Shiyuan Agrawal, Divyakant El Abbadi, Amr
Abstract The back-end databases of web-based applications are a major data security concern to enterprises. The problem becomes more critical with the proliferation of enterprise hosted web applications in the cloud. While prior work has concentrated on malicious attacks that try to break into the database using vulnerabilities of web applications, little work has focused on the threat of data harvesting through web form interfaces, in which large collections of the underlying data can be harvested and sensitive information can be learnt by iteratively submitting legitimate queries and analyzing the returned results for designing new queries. To defend against data harvesting without compromising usability, we consider a detection approach. We summarize the characteristics of data harvesting, and propose the notions of query correlation and result coverage for data harvesting detection. We design a detection system called HengHa, in which Heng examines the correlation among queries in a session, and Ha evaluates the data coverage of the results of queries in the same session. The experimental results verify the effectiveness and efficiency of HengHa for data harvesting detection.
Starting Page 59
Ending Page 64
Page Count 6
File Format PDF
ISBN 9781450300896
DOI 10.1145/1866835.1866847
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-10-08
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Crawling Result coverage Sampling Data harvesting detection Query correlation
Content Type Text
Resource Type Article
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