6,891 research outputs found
Review on Present State-of-the-Art of Secure and Privacy Preserving Data Mining Techniques
As people of every walk of life are using Internet for various purposes there is growing evidence of proliferation of sensitive information. Security and privacy of data became an important concern. For this reason privacy preserving data mining (PPDM) has been an active research area. PPDM is a process discovering knowledge from voluminous data while protecting sensitive information. In this paper we explore the present state-of-the-art of secure and privacy preserving data mining algorithms or techniques which will help in real world usage of enterprise applications. The techniques discussed include randomized method, k-Anonymity, l-Diversity, t-Closeness, m-Privacy and other PPDM approaches. This paper also focuses on SQL injection attacks and prevention measures. The paper provides research insights into the areas of secure and privacy preserving data mining techniques or algorithms besides presenting gaps in the research that can be used to plan future research
Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory
In Internet of Vehicles (IoV), data sharing among vehicles is essential to
improve driving safety and enhance vehicular services. To ensure data sharing
security and traceability, highefficiency Delegated Proof-of-Stake consensus
scheme as a hard security solution is utilized to establish blockchain-enabled
IoV (BIoV). However, as miners are selected from miner candidates by
stake-based voting, it is difficult to defend against voting collusion between
the candidates and compromised high-stake vehicles, which introduces serious
security challenges to the BIoV. To address such challenges, we propose a soft
security enhancement solution including two stages: (i) miner selection and
(ii) block verification. In the first stage, a reputation-based voting scheme
for the blockchain is proposed to ensure secure miner selection. This scheme
evaluates candidates' reputation by using both historical interactions and
recommended opinions from other vehicles. The candidates with high reputation
are selected to be active miners and standby miners. In the second stage, to
prevent internal collusion among the active miners, a newly generated block is
further verified and audited by the standby miners. To incentivize the standby
miners to participate in block verification, we formulate interactions between
the active miners and the standby miners by using contract theory, which takes
block verification security and delay into consideration. Numerical results
based on a real-world dataset indicate that our schemes are secure and
efficient for data sharing in BIoV.Comment: 12 pages, submitted for possible journal publicatio
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