On Intercept Probability Minimization Under Sparse Random Linear Network Coding

Tassi, Andrea; Piechocki, Robert J.; Nix, Andrew R

doi:10.1109/tvt.2019.2907290

“…Given that our focus is on the 𝑁 − 𝑁 E erroneously received coded packets, we use the set E to isolate the 𝑁 − 𝑁 E of the 𝑁 rows of Y, X, E and H, and construct Y E , X E , E E and H E . As a result, expressions (7) and ( 10) change to:…”

Section: Eavesdropping Enhanced By Pprmentioning

confidence: 99%

The Impact of Partial Packet Recovery on the Inherent Secrecy of Random Linear Coding

Chatzigeorgiou

¹

2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

0

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This paper considers a source, which employs random linear coding (RLC) to encode a message, a legitimate destination, which can recover the message if it gathers a sufficient number of coded packets, and an eavesdropper. The probability of the eavesdropper accumulating enough coded packets to recover the message, known as the intercept probability, has been studied in the literature. In our work, the eavesdropper does not abandon its efforts to obtain the source message if RLC decoding has been unsuccessful; instead, it employs partial packet recovery (PPR) offline in an effort to repair erroneously received coded packets before it attempts RLC decoding again. Results show that PPR-assisted RLC decoding marginally increases the intercept probability, compared to RLC decoding, when the channel conditions are good. However, as the channel conditions deteriorate, PPR-assisted RLC decoding significantly improves the chances of the eavesdropper recovering the source message, even if the eavesdropper experiences similar or worse channel conditions than the destination.

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“…The other main alternative to reducing the RLNC computational complexity is to utilize sparse coding coefficient rows, with only a relatively small prescribed number of non-zero coding coefficients. Sparse RLNC [73]- [78] has been examined in a range of contexts, including broadcast systems [79]- [81], data compression [82], and secure communications [83]. The decoding probability and delay for sparse RLNC have been analyzed in [84]- [88], while the tuning of the sparsity has been examined in [89]- [92], and sparsity for sliding window RLNC [42]- [47] has been studied in [93], [94].…”

Section: B Related Workmentioning

confidence: 99%

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

Nguyen

¹

,

Tasdemir

²

,

Nguyen

³

et al. 2020

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Fulcrum coding combines a high-field outer Random Linear Network Coding (RLNC) that generates outer coding expansion packets with a small-field inner RLNC that combines the source packets and the outer coding expansion packets. This two-layer Fulcrum coding allows flexible decoding in receivers with heterogeneous computational capabilities. Fulcrum coding has so far only been studied for conventional dense RLNC, which randomly selects all coding coefficients, and only for a statically fixed number of outer expansion packets. However, the probability that the coding coefficient row of a newly received packet is linearly independent of prior received coding coefficient rows (a prerequisite for successful decoding) is highly dynamic. We propose to exploit the dynamics of this probability to reduce the computational complexity of Fulcrum coding. In particular, we vary the density of non-zero coding coefficients, i.e., equivalently, the sparsity of coding coefficients, and the number of outer expansion packets to keep the complexity low while maintaining a reasonably high decoding probability. We introduce the general principles of dynamic sparsity and expansion packets (DSEP) for Fulcrum coding as well as two specific example DSEP policies. Our evaluations indicate that DSEP Fulcrum can increase the encoding throughput tenfold and increase the decoding throughput 1.4 to 4.3 fold while achieving decoding probabilities that are typically less than 1% lower than the conventional Fulcrum decoding probabilities. We also find that DSEP achieves somewhat higher encoding and decoding throughputs than the CodornicesRq (Release 2.1) implementation of RaptorQ block coding for small blocks (generations) of source packets, while RaptorQ is substantially faster for large generation sizes. Furthermore, we develop and evaluate an elementary DSEP recoding mechanism that achieves a recoding throughput more than double the decoding throughput.

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“…The analysis in [4] was later revisited and refined by Khan et al [5]. Since then, the intercept probability has been analyzed in different settings; for example, layered RLC was studied in [6], sparse RLC was investigated in [7] and applications of secure RLC-encoded data transfer were explored in [8], [9].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Partial Packet Recovery on the Inherent Secrecy of Random Linear Coding

Chatzigeorgiou¹

2022

Preprint

0

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This paper considers a source, which employs random linear coding (RLC) to encode a message, a legitimate destination, which can recover the message if it gathers a sufficient number of coded packets, and an eavesdropper. The probability of the eavesdropper accumulating enough coded packets to recover the message, known as the intercept probability, has been studied in the literature. In our work, the eavesdropper does not abandon its efforts to obtain the source message if RLC decoding has been unsuccessful; instead, it employs partial packet recovery (PPR) offline in an effort to repair erroneously received coded packets before it attempts RLC decoding again. Results show that PPR-assisted RLC decoding marginally increases the intercept probability, compared to RLC decoding, when the channel conditions are good. However, as the channel conditions deteriorate, PPR-assisted RLC decoding significantly improves the chances of the eavesdropper recovering the source message, even if the eavesdropper experiences similar or worse channel conditions than the destination.

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On Intercept Probability Minimization Under Sparse Random Linear Network Coding

Cited by 9 publications

References 8 publications

The Impact of Partial Packet Recovery on the Inherent Secrecy of Random Linear Coding

The Impact of Partial Packet Recovery on the Inherent Secrecy of Random Linear Coding

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

The Impact of Partial Packet Recovery on the Inherent Secrecy of Random Linear Coding

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