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Information Security – Theory vs. Reality , Winter 2011 Guest Lecturer: Yossi Oren 1

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AES Circuit Design Statistics Introduction to Power Analysis 4

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PlaintextCiphertext Key AES 5

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Source: 6

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⇐ Low Variance High ⇒ Variance ⇐ Low Correlation High ⇒ Correlation 8

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Power Vibration Timing Sound Heat EM PlaintextCiphertext Radiation Crypto Device Key Bad InputsErrors 9

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Power consumption is variable Power consumption depends on instruction Power consumption depends on data 10

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q Power consumption V dd GND a q A P1 C1 C2 N1 The power consumption of a CMOS gate depends on the data: q: 0->0 virtually no power cons. q: 1->1 virtually no power cons. q: 0->1 high power cons. (proportional to C2) q: 1->0 high power cons. (proportional to C1)

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Source: DPA Book 12

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Source: DPA Book 13

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Source: DPA Book 14

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AES Circuit Design Statistics 15

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Simple Power Analysis Warm-up Correlation Power Analysis Full Correlation Power Analysis 17

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Plaintexts and ciphertexts may be chosen, known or unknown Power PlaintextsCiphertexts Crypto Device Key 18

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Power consumption is variable Power consumption depends on instruction Power consumption depends on data 19

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Pros: Small amount of traces Cons: Detailed reverse engineering Long manual part 20

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Use statistical properties of traces to recover key Pros: Very limited reverse engineering Harder to confuse Cons: Large amount of traces Two main types of DPA: Difference of means (traditional DPA) Correlation power analysis (CPA) 21

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We want to discover the correct key value (c k ) and when it is used (c t ) Idea: On the correct time, the power consumption of all traces is correlated with the correct key On other times and other keys the traces should show low correlation 22

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Assume plaintext and correct key are known but correct time is unknown Form hypothesis and test it Good hypothesis: Depends on known plaintext Depends on small amount of key bits Non-linear – sensitive to small changes Maps to power consumption using a model 23

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1000 traces, each consisting of 1 million points Each trace uses a different known plaintext – 1000 plaintexts 1 known key Hypothesis is vector of 1000 hypothetical power values Output of warm-up CPA: vector of 1 million correlation values with peak at c t 24

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Plaintext is known, but correct key and correct time unknown Idea: run warm-up CPA many times in parallel Create many competing hypotheses 26

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1000 traces, each consisting of 1 million points Each trace uses a different known plaintext – 1000 plaintexts Key is unknown – 256 guesses for first byte Hypothesis is matrix of 1000X256 hypothetical power values Output of full CPA: matrix of 1,000,000X256 correlation values with peak at (c k,c t ) 27

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Simple Power Analysis Warm-up Correlation Power Analysis Full Correlation Power Analysis 29

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