AccountTrade: Accountability Against Dishonest Big Data Buyers and Sellers

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: 

·         System : Pentium Dual Core.

·         Hard Disk : 120 GB.

·         Monitor : 15’’ LED

·         Input Devices : Keyboard, Mouse

·         Ram : 1 GB

SOFTWARE REQUIREMENTS: 

·         Operating system : Windows 7.

·         Coding Language : JAVA/J2EE

·         Tool : Netbeans 7.2.1

·         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.