Distributed Source Coding in the Presence of Byzantine Sensors

The distributed source coding problem is considered when the sensors, or encoders, are under Byzantine attack; that is, an unknown group of sensors have been reprogrammed by a malicious intruder to undermine the reconstruction at the fusion center. Three different forms of the problem are considered. The first is a variable-rate setup, in which the decoder adaptively chooses the rates at which the sensors transmit. An explicit characterization of the variable-rate achievable sum rates is given for any number of sensors and any groups of traitors. The converse is proved constructively by letting the traitors simulate a fake distribution and report the generated values as the true ones. This fake distribution is chosen so that the decoder cannot determine which sensors are traitors while maximizing the required rate to decode every value. Achievability is proved using a scheme in which the decoder receives small packets of information from a sensor until its message can be decoded, before moving on to the next sensor. The sensors use randomization to choose from a set of coding functions, which makes it probabilistically impossible for the traitors to cause the decoder to make an error. Two forms of the fixed-rate problem are considered, one with deterministic coding and one with randomized coding. The achievable rate regions are given for both these problems, and it is shown that lower rates can be achieved with randomized coding.

[1]  Zygmunt J. Haas,et al.  Securing ad hoc networks , 1999, IEEE Netw..

[2]  Lang Tong,et al.  Distributed Inference in the Presence of Byzantine Sensors , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[3]  Jack K. Wolf,et al.  Noiseless coding of correlated information sources , 1973, IEEE Trans. Inf. Theory.

[4]  Thomas M. Cover,et al.  A Proof of the Data Compression Theorem of Slepian and Wolf for Ergodic Sources , 1971 .

[5]  Radia J. Perlman,et al.  Network layer protocols with Byzantine robustness , 1988 .

[6]  Adrian Perrig,et al.  Security and Privacy in Sensor Networks , 2003, Computer.

[7]  Baruch Awerbuch,et al.  An on-demand secure routing protocol resilient to byzantine failures , 2002, WiSE '02.

[8]  A. D. Wyner,et al.  The wire-tap channel , 1975, The Bell System Technical Journal.

[9]  Danny Dolev,et al.  The Byzantine Generals Strike Again , 1981, J. Algorithms.

[10]  Tracey Ho,et al.  Byzantine modification detection in multicast networks using randomized network coding , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[11]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[12]  Aaron D. Wyner,et al.  The common information of two dependent random variables , 1975, IEEE Trans. Inf. Theory.

[13]  Lang Tong,et al.  Capacity of Cooperative Fusion in the Presence of Byzantine Sensors , 2006, ArXiv.

[14]  Toby Berger,et al.  The CEO problem [multiterminal source coding] , 1996, IEEE Trans. Inf. Theory.

[15]  Giuseppe Longo,et al.  The information theory approach to communications , 1977 .

[16]  Tracey Ho,et al.  Correction of adversarial errors in networks , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..