This document proposes a unified framework for approximating the optimal key estimation of stream ciphers using probabilistic inference. It formulates the key estimation problem as determining the secret key that maximizes the joint probability based on the observed keystream. An approximation algorithm called the sum-product algorithm is used to efficiently compute approximate marginal probabilities on a factor graph representing the cipher structure. Preprocessing techniques can reduce the complexity of the sum-product algorithm when applied to ciphers using linear feedback shift registers.
This document analyzes the asymptotic properties of expected cumulative logarithmic loss in Bayesian estimation when models are nested and when there is misspecification. The main theorem states that if the true distribution does not belong to the model class, the asymptotic loss per symbol goes to the Kullback-Leibler divergence between the true and model distributions, rather than 0. If the true distribution does belong to the model class, the results reduce to previous studies. The proof is separated into two parts and relies on a lemma showing posterior concentration at the true model.
This document proposes a unified framework for approximating the optimal key estimation of stream ciphers using probabilistic inference. It formulates the key estimation problem as determining the secret key that maximizes the joint probability based on the observed keystream. An approximation algorithm called the sum-product algorithm is used to efficiently compute approximate marginal probabilities on a factor graph representing the cipher structure. Preprocessing techniques can reduce the complexity of the sum-product algorithm when applied to ciphers using linear feedback shift registers.
This document analyzes the asymptotic properties of expected cumulative logarithmic loss in Bayesian estimation when models are nested and when there is misspecification. The main theorem states that if the true distribution does not belong to the model class, the asymptotic loss per symbol goes to the Kullback-Leibler divergence between the true and model distributions, rather than 0. If the true distribution does belong to the model class, the results reduce to previous studies. The proof is separated into two parts and relies on a lemma showing posterior concentration at the true model.
This document proposes a linear programming (LP) based approach for solving maximum a posteriori (MAP) estimation problems on factor graphs that contain multiple-degree non-indicator functions. It presents an existing LP method for problems with single-degree functions, then introduces a transformation to handle multiple-degree functions by introducing auxiliary variables. This allows applying the existing LP method. As an example, it applies this to maximum likelihood decoding for the Gaussian multiple access channel. Simulation results demonstrate the LP approach decodes correctly with polynomial complexity.
The document proposes a new method for document classification with small training data. It discusses previous methods that estimate parameters for prior distributions either using fixed values or estimating data. The new proposed method estimates parameters for prior distributions as a weighted combination of estimating data and training data. Experiments show the new method achieves higher accuracy than previous methods, especially with small training data sizes.
The document proposes a method to calculate the theoretical throughput limit of type-I hybrid selective-repeat ARQ with a finite receiver buffer using Markov decision processes. The authors model the problem as an MDP and develop an algorithm to compute the maximum expected utility and throughput limit by applying dynamic programming. Simulation results show the throughput of previous methods approaches the proposed theoretical limit with increasing buffer size.
The document proposes reducing the computational complexity of message passing algorithms like belief propagation (BP) and concave-convex procedure (CCCP) for multiuser detection in CDMA systems. It does this by changing the factor graph structure used to represent the detection problem from a fully connected graph (Factor Graph I) to a sparsely connected graph (Factor Graph II). Simulation results show the proposed CCCP detector for the new factor graph achieves near optimal performance with lower complexity than existing approaches.
5. 3.問題設定(1/2)
? 擬似乱数生成器の確率モデル
s ? {0,1}L :鍵
z ? {0,1}N :鍵系列
P ( z | s)
P (s) s 擬似乱数 z
生成器
ある確率分布に
従って発生 z は s から確定的
?0
P ( z | s) ? ?
? 既知平文攻撃 ?1
既知: z , P (s) , P (z | s)
未知: s
既知情報から未知 s を求める.
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6. 3.問題設定(2/2)
? 統計的決定理論に基づく最適な鍵推定 [Ety ’10]
s ? {0,1}L :鍵の推定値
?
決定関数 s ? ? (z )
?
最適な決定関数 ? (z ) →平均誤り率最小
?
事後確率最大とする決定
? ? (z ) ? arg max P(s | z )
s
? arg max P (s, z ) (∵ベイズの定理)
s
擬似乱数生成器を同時確率関数で表現
鍵推定アルゴリズムの提案
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8. 4.擬似乱数生成器(2/4)
? 非線形コンバイナ型乱数生成器(NCG)
K個のLFSRと1個のK入力1出力非線形関数から構成される.
z n ? {0,1}:鍵系列のnビット目
xn1)
(
LFSR(1) zn
非線形
関数f
xn K )
(
LFSR(K)
f : {0,1}K ? {0,1}
? の同時確率関数
LFSR(k)の出力系列 が偽
8 の同時確率 が真
9. 4.擬似乱数生成器(3/4)
? E0
4個のLFSRと1個の有限状態機械(FSM)から構成される.
y n :n時点のLFSRの出力の総和
? n :n時点のFSMの状態
xn1)
(
LFSR(1) yn
( 4)
+ FSM ? n ?1
x
?n
n
LFSR(4)
zn
FSMの状態遷移関数
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12. 5.確率推論アルゴリズムに基づく鍵推定(2/5)
? ファクターグラフ
関数の因数分解の構造を表現したグラフ.
例 g ( x1 , x2 , x3 , x4 ) ? f A ( x1 , x2 , x3 ) f B ( x2 , x3 , x4 )
fA fB
○:変数ノード
■:因数ノード
x1 x2 x3 x4
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13. 5.確率推論アルゴリズムに基づく鍵推定(3/5)
? 擬似乱数生成器の同時確率関数のファクターグラフ
? LFSR部分 ? E0
xn2 )
(
xn3)
(
x (k )
x (k )
x (k ) xn1)
(
xn4 )
(
1 2 N
? NCGの非線形関数部分 yn
xn1)
(
?n ? n ?1
zn
xn K )
(
x ( 2)
n
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14. 5.確率推論アルゴリズムに基づく鍵推定(4/5)
? sum-productアルゴリズム(1/2)
ファクターグラフ上でメッセージと呼ばれる値を伝搬させること
で周辺確率を効率的に計算するアルゴリズム.
? x? f (x) :xからfへのメッセージ
? f ? x (x) :fからxへのメッセージ
→初期値を適当に仮定 ? x? f (x) f
? 変数ノード→因数ノードの更新 x
? f ? x (x)
n( x ) { f } n( f ) {x}
? 因数ノード→変数ノードの更新
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15. 5.確率推論アルゴリズムに基づく鍵推定(5/5)
? sum-productアルゴリズム(2/2)
? 周辺確率の計算
最大 I 回のメッセージ更新後,以下を計算.( I は予め設定)
x
なし???真の周辺確率
→ファクターグラフにループ
あり???近似周辺確率
の大きい方を鍵のビットとして推定
? 計算量
ファクターグラフに含まれる枝数の指数オーダ
→LFSR部分の枝数が非常に多い=計算量:大
基本的に鍵の一部(Bビット)を全数探索
→前処理として[Mihaljevic ’01]の方法を用いることで枝数削減
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16. 6.シミュレーション(1/3)
? シミュレーション内容
? 同じLFSRの組を用いたNCGとE0について攻撃
K ?4
? パラメータ
L :LFSRの長さの合計(鍵のビット数) 固定
N :観測された鍵系列長
B :全数探索にあてるビット数 変化
? それぞれ1000回ずつ攻撃を行う.
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