1 / 18

CSC 774 Advanced Network Security

CSC 774 Advanced Network Security. Topic 2.4 Rabin’s Information Dispersal Algorithm. Slides by Sangwon Hyun. Motivation. IDA was developed to provide safe and reliable transmission of information in distributed systems. Inefficiency of retransmission of lost packets

seth-mendez
Download Presentation

CSC 774 Advanced Network Security

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. CSC 774 Advanced Network Security Topic 2.4 Rabin’s Information Dispersal Algorithm Slides by Sangwon Hyun Dr. Peng Ning

  2. Motivation • IDA was developed to provide safe and reliable transmission of information in distributed systems. • Inefficiency of retransmission of lost packets • In multicast transmission, different receivers lose different sets of packets. • Re-request and retransmission increases delays. • Forward error correction technique might be desirable in distributed systems. Dr. Peng Ning

  3. Basic Idea of IDA Dr. Peng Ning

  4. Dispersal(F, m, n) • Let F be a data of size N in byte (|F|=N). • m should be less than or equal to n (m ≤ n). • Dispersal(F, m, n): • splitting the data F with some amount of redundancy resulting in n pieces Fi(1 ≤ i ≤ n). • |Fi|=|F|/m • Thus, the size of F, N, should be a multiple of m. Dr. Peng Ning

  5. Dispersal(F, 4, 8) F1 F2 F3 F4 F5 F6 F7 F8 Dispersal(F, m, n) – Example 1 • |F|=32 bytes, m=4, n=8 F • |Fi| = 32/4 = 8 bytes (1 ≤ i ≤ n) Dr. Peng Ning

  6. Recovery({Fij |(1≤ j ≤m), (1≤ ij ≤n)}, m, n) • Recovery({Fij |(1≤ j ≤m), (1≤ ij ≤n)}, m, n): • reconstructing the original data F from any m pieces among n pieces (Fi(1 ≤ i ≤ n)) Dr. Peng Ning

  7. F1 F3 F4 F7 Recovery({F1, F3, F4, F7}, 4, 8) Recovery({Fij |(1≤ j ≤m), (1≤ ij ≤n)}, m, n) – Example 2 • |F|=32 bytes, m=4, n=8, |Fi|=8 bytes(1 ≤ i ≤ 8) • Let us assume that the following 4(=m) pieces are received. F Dr. Peng Ning

  8. Detailed Operations Dr. Peng Ning

  9. Dispersal(F, m, n) • F = b1,b2,…,bN • |F|=N, and bi represents each byte in F (0 ≤ bi ≤ 255). • All computations should be done in GF(28). • GF(28) is closed under addition and multiplication. • Every nonzero element in GF(28) has a multiplicative inverse. • F = (b1,…,bm),(bm+1,…,b2m),…,(bN-m+1,…,bN) • Si = (b(i-1)m+1,…,bim) T(1 ≤ i ≤ N/m) • The matrix, M (m× N/m), is constructed as follows: • M = [ S1 S2 … SN/m] Dr. Peng Ning

  10. Dispersal(F, m, n) • The matrix, A (n×m), is constructed as follows: • ai = (ai1, …,aim) (1 ≤ i ≤ n) • chosen such that every subset of m different vectors are linearly independent. Dr. Peng Ning

  11. Dispersal(F, m, n) • The following Vandermonde matrix satisfies the property required for A. • m ≤ n, and all xi’s are nonzero elements in GF(28) and pairwise different. • Any m different rows are linearly independent, so any matrix composed of a set of any m different rows is invertible. Dr. Peng Ning

  12. Dispersal(F, m, n) • n pieces, Fi (1 ≤ i ≤ n), are computed as follows: • ai・Sk = (ai1b(k−1)m+1 + …+ aimbkm) Dr. Peng Ning

  13. Dispersal(F, m, n) – Example 3 • |F|=32 bytes, m=4, n=8 • F = b1,b2,…,b32 • F = (b1,…,b4),(b5,…,b8),…,(b29,…,b32) • M (4×8) Dr. Peng Ning

  14. Dispersal(F, m, n) – Example 3 • A (8×4) Dr. Peng Ning

  15. Dispersal(F, m, n) – Example 3 • Fi (1 ≤ i ≤ 8) are computed as follows: Dr. Peng Ning

  16. Recovery({Fij |(1≤ j ≤m), (1≤ ij ≤n)}, m, n) • Given m pieces Fij ( (1≤ j ≤m), (1≤ ij ≤n) ), • M can be recovered from the given m pieces Fij ( (1≤ j ≤m), (1≤ ij ≤n) ) because A’ is invertible. Dr. Peng Ning

  17. Recovery({Fij |(1≤ j ≤m), (1≤ ij ≤n)}, m, n) – Example 4 • |F|=32 bytes, m=4, n=8 • In example 3, Fi (1 ≤ i ≤ 8) pieces of 8 bytes are resulted. • Assume that {F1,F3,F4,F7} are received among them. Dr. Peng Ning

  18. Recovery({Fij |(1≤ j ≤m), (1≤ ij ≤n)}, m, n) – Example 4 • The original data M can be recovered by the following computation: Dr. Peng Ning

More Related