A Privacy-preserving Method for Encrypted Traffic Feature Set Based on Laplace Mechanism
As network security and privacy issues are widely concerned,more and more network traffic is transmitted by using encryption technology.The classification of encrypted traffic plays a vital role in network supervision.In or-der to solve the problems of user privacy leakage that are easy to occur in the process of encrypted traffic classifica-tion,a privacy protection method of encrypted traffic feature set based on Laplace mechanism was proposed.In this method,the label bar of the encrypted traffic feature set is disturbed to a certain extent according to the generated in-terval by generating random disturbance interval intervals,so as to protect user privacy information.Finally,the pro-posed method is verified on the ISCX VPN-NonVPN dataset.The experimental results show that the accuracy of en-crypted traffic classification can be well guaranteed under the premise of ensuring privacy,which proves the effective-ness and usability of the proposed method.
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