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On Darwinism and Side Channel Resistance

On Darwinism and Side Channel Resistance

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On Darwinism and Side Channel Resistance

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  1. On Darwinism and Side Channel Resistance RHUL 2013 Talk David Naccache

  2. MüllerianMimicry “A natural phenomenon in which two or more poisonous species, that may or may not be related and share one or more common predators, have come to mimic each other's warning signals.” (Named after German naturalist Fritz Müller, who proposed the concept in 1878).

  3. ant

  4. extra legs spider antennae ant

  5. Physics µP Memory data Leakage function

  6. Physics digital information µP Memory data pure leakage noise Leakage function measuredleakage

  7. Model digital information µP Memory data pure leakage noise Leakage function measuredleakage

  8. Model bits µP Memory data pure leakage noise Leakage function measuredleakage

  9. Digital MüllerianSimilarity Rules: attack and defense have the samerights: • Both Have the sameknowledge about the system • Bothcanexperiment as much as theywishwith the system. • Insteadof transmitting bits, the defender sacrifices part of hisbandwidth to buysimilarity. camouflage value The -bit number

  10. The Game Defender’s goal: for each select a . For the values must besuchthat: is as similar as possible to

  11. The Game Defender’s goal: for each select a . For the values must besuchthat: is as similar as possible to Be as discreet as possible and let noise do the rest of the cover-up work!

  12. A Small Example Consider bits. Wedevote 2 bits to payload data (i.e. ) and the 2 other bits to camouflage (i.e. )

  13. A Small Example Consider bits. Wedevote 2 bits to payload data (i.e. 0,1,2,3) and the 2 other bits to camouflage (i.e. ) We call these « colors »

  14. A Small Example We have sixteen values belonging to four color groups. We need to select one representative in each group 0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111

  15. A Small Example We have sixteen values belonging to four color groups. We need to select one representative in each group 0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111 species cydna species melopomene species erato species sapho

  16. Apply Natural Selection We have sixteen values belonging to four color groups. We need to select one representative in each group 0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111 species cydna species melopomene species erato species sapho

  17. Apply Natural Selection Criterion: Analogy: MSB = basic genotype, LSB = mutations 0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111 species cydna species melopomene species erato species sapho

  18. How to Find the Good One? Simulating Mutations iseasy, measuringsimilarityis not! Assume that a simplisticbluejay has only one criterion: the global intensity of yellowin the butterfly. 0000 0 0001 0 0010 1 0011 3 0100 0 0101 1 0110 1 0111 4 1000 3 1001 9 1010 7 1011 4 1100 3 1101 9 1110 4 1111 2

  19. Sort Butterflies by Yellowness 0 6 1 2 3 4 5 7 8 9

  20. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. 0 6 1 2 3 4 5 7 8 9

  21. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. 0 6 1 2 3 4 5 7 8 9 No

  22. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. 0 6 1 2 3 4 5 7 8 9 No

  23. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. 0 6 1 2 3 4 5 7 8 9 No

  24. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 Yes

  25. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 Yes

  26. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 No

  27. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 Yes

  28. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 Yes

  29. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 No

  30. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 No

  31. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 No

  32. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 No

  33. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 No

  34. Sort Butterflies by Yellowness Problem: Find the shortest segment containing all colors A quadratic-time algorithm: just scan and keep an optimum. Current optimum 0 6 1 2 3 4 5 7 8 9 No

  35. Sort Butterflies by Yellowness Indeed: 0 6 1 2 3 4 5 7 8 9

  36. If The Blue Jay is More Perceptive?

  37. If The Blue Jay is More Perceptive? Cyanocittacristata oculus-laserisperceives both size and yellow levels.

  38. Well… Now Trouble Starts In terms of similarity, how many yellow units worth one size unit?

  39. Well… Now Trouble Starts Even if we simplify and assume that 1 size unit = 1 yellow unit how do we find the optimum?

  40. Well… Now Trouble Starts Even if we simplify and assume that 1 size unit = 1 yellow unit how do we find the optimum? Even worse: the Blue Jay’s perception of size and yellow might even be negatively correlated! i.e. if sizes are hard to distinguish then yellows are not and conversely.

  41. Well… Now Trouble Starts Even if we simplify and assume that 1 size unit = 1 yellow unit how do we find the optimum? Even worse: the Blue Jay’s perception of size and yellow might even be negatively correlated! i.e. if sizes are hard to distinguish then yellows are not and conversely. Luckily this can be dealt with.

  42. Well… Now Trouble Starts Even if we simplify and assume that 1 size unit = 1 yellow unit how do we find the optimum? Even worse: the Blue Jay’s perception of size and yellow might even be negatively correlated! i.e. if sizes are hard to distinguish then yellows are not and conversely. Luckily this can be dealt with. For now we assume equal weights and independence.

  43. The New Problem Here each is a side channel curve with 2 samples Power Power Sample 1 Time Power Sample 2

  44. The New Problem Here each is a side channel curve with 2 samples Power Sample 1 Power Sample 2

  45. The New Problem Find the smallest circle containing all colors Size Level of yellow

  46. Note: easygeneralization to more properties (willbeseelater) Find the smallest sphere containing all colors