ABSTRACT:
In this paper, a set of accountable protocols denoted as AccountTrade
is proposed for big data trading among dishonest consumers. For achieving
secure the big data trading environment, AccountTrade
achieves book-keeping ability and accountability against dishonest consumers
throughout the trading (i.e., buying and selling) of datasets. We investigate
the consumers’ responsibilities in the dataset trading, then
we design AccountTrade to achieve accountability
against dishonest consumers that are likely to deviate from the
responsibilities. Specifically, a uniqueness index is defined and proposed,
which is a new rigorous measurement of the data uniqueness for this purpose.
Furthermore, several accountable trading protocols are
presented to enable data brokers to blame the misbehaving entities when misbehavior is detected. The accountability of AccountTrade is formally defined, proved, and evaluated by
an automatic verification tool as well as extensive simulation with real-world
datasets. Our evaluation shows that AccountTrade
incurs at most 10KB storage overhead per file, and it is capable of 8-1000 concurrent data upload requests per server.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Taeho Jung, Member, IEEE, Xiang-Yang Li, Fellow, IEEE, Wenchao
Huang, Zhongying Qiao, Jianwei Qian, Linlin
Chen, Junze Han, Jiahui Hou, “AccountTrade:
Accountability Against Dishonest Big Data Buyers and Sellers”, IEEE
Transactions on Information Forensics and Security, 2018.