A Dummy-Based Approach for Preserving Source Rate Privacy

被引:16
作者
Diyanat, Abolfazl [1 ,2 ]
Khonsari, Ahmad [1 ,2 ]
Shariatpanahi, Seyed Pooya [2 ]
机构
[1] Univ Tehran, Dept Elect & Comp Engn, Tehran 1417614418, Iran
[2] Inst Res Fundamental Sci, Sch Comp Sci, Tehran 1953833511, Iran
关键词
Dummy packets; Fano's inequality; flow conservation law; preemptive resume 2-priority queue; rate privacy;
D O I
10.1109/TIFS.2016.2515050
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Recent studies reveal that an adversary might trace the apparently insignificant traffic rate of source nodes over the net and turn such data to invaluable information so as to breach the privacy of the victim sources. Inhibiting the adversary of being able to extract information from the traffic rate of source nodes is a complicated task unless taking into consideration the flow conservation law effect of the transmitter queue. A reliable method of preserving the rate privacy that copes with the flow conservation law is to transmit original packets augmented with probabilistically dummy ones so as to change the observable aggregated traffic rate. Augmenting dummy packets, however, bears redundancy, and hence, requires extra resources in terms of bandwidth and buffer requirements, and more importantly suggests higher transmitting energy consumption. Grounded on the queueing and information theories, in this paper, we present an efficient method that minimally augments dummy packets to preserve the source rate privacy at a given degree while preserving the delay distribution of the original packets intact, and thus does not affect the quality of service parameters of the transmitted data in terms of delay and jitter. The presented method models the original packets and dummy ones with a preemptive resume 2-priority queueing system and then using information theory attempts to maximize the Fano lower bound of the best estimation of the adversary's speculation. All of the theoretically obtained results have been validated by conducting simulation experiments.
引用
收藏
页码:1321 / 1332
页数:12
相关论文
共 31 条
[1]  
Aggarwal Charu C, 2008, A general survey of privacy-preserving data mining models and algorithms
[2]  
[Anonymous], 1995, Probability, stochastic processes, and queueing theory: the mathematics of computer performance modeling
[3]  
Baccelli F., 2003, STOCHASTIC MODELLING
[4]  
Cover TM., 1999, ELEMENTS INFORM THEO, DOI DOI 10.1002/047174882X
[5]  
Dainotti A., 2011, PROC IEEE GLOBAL TEL, P1
[6]   On Adversary Models and Compositional Security [J].
Datta, Anupam ;
Franklin, Jason ;
Garg, Deepak ;
Jia, Limin ;
Kaynar, Dilsun .
IEEE SECURITY & PRIVACY, 2011, 9 (03) :26-32
[7]   Privacy-Preserving Data Publishing: A Survey of Recent Developments [J].
Fung, Benjamin C. M. ;
Wang, Ke ;
Chen, Rui ;
Yu, Philip S. .
ACM COMPUTING SURVEYS, 2010, 42 (04)
[8]  
Gaikwad S. M., 2014, NETW COMPLEX SYST, V4, P1
[9]  
Graham R. L., 2000, CONCRETE MATH FDN CO
[10]  
Harchol-Balter Mor, 2013, Performance Modeling and Design of Computer Systems, V1st