城市地下空间工程中英文对照外文翻译文献

中英文对照外文翻译

(文档含英文原文和中文翻译)

外文文献：

Correlation Power Analysis with a Leakage Model Abstract.A classical model is used for the power consumption of cryptographic devices. It is based on the Hamming distance of the data handled with regard to an unknown but constant reference state. Once validated experimentally it allows an optimal attack to be derived called Correlation Power Analysis. It also explains the defects of former approaches such as differential Power Analysis.

Keywords:Correlation factor, CPA, DPA, Hamming distance, power analysis,

DES, AES, secure cryptographic device, side channel.

1 Introduction

In the scope of statistical power analysis against cryptographic devices, two historical trends can be observed. The ?rst one is the well known di fferential power analysis (DPA)

introduced by Paul Kocher [12,13] and formalized by Thomas Messerges etal. [16]. The second one has been suggested in various papers [8,14,18] and proposed to use the correlation factor between the power samples and the Hamming weight of the handled data. Both approaches exhibit some limitations due to unrealistic assumptions and model imperfections that will be examined more thoroughly in this paper. This work follows previous studies aiming at either improving the Hamming weight model [2], or enhancing the DPA itself by various means [6,4].

The proposed approach is based on the Hamming distance model which can be seen as a generalization of the Hamming weight model. All its basic assumptions were already mentioned in various papers from year 2000 [16,8,6,2]. But they remained allusive as possible explanation of DPA defects and never leaded to any complete and convenient exploitation. Our experimental work is a synthesis of those former approaches in order to give a full insight on the data leakage. Following [8,14,18] we propose to use the correlation power analysis (CPA) to identify the parameters of the leakage model. Then we show that sound and efficient attacks can be conducted against unprotected implementations of many algorithms such as DES or AES. This study deliberately restricts itself to the scope of secret key cryptography although it may be extended beyond.

This paper is organized as follows: Section 2 introduces the Hamming distance model and Section 3 proves the relevance of the correlation factor. The model based correlation attack is described in Section 4 with the impact on the model errors. Section 5 addresses the estimation problem and the experimental results which validate the model are exposed in Section 6. Section 7 contains the comparative study with DPA and addresses more specifically the so-called ―ghost peaks‖ problem encountered by those who have to deal with erroneous conclusions when implementing classical DPA on the substitution boxes of the DES ?rst round: it is shown there how the proposed model explains many defects of the DPA and how the correlation power analysis can help in conducting sound attacks in optimal conditions. Our conclusion summarizes the advantages and drawbacks of CPA versus DPA and reminds that countermeasures work against both methods as well.

2 The Hamming Distance Consumption Model

Classically, most power analyses found in literature are based upon the Hamming weight model [13,16], that is the number of bits set in a data word. In a m-bit microprocessor, binary data is coded ∑-==1

02m j j j d D , with the bit values dj=0 or 1. Its Hamming weight is simply the number of bits set to 1,∑-==10)(m j j d D H Its integer values stand between 0 and m. If D contains m independent and uniformly distributed bits, the whole word has an average Hamming

weight 2/m H =μand a variance 4/2m H =σ. It is generally assumed that the data leakage through the power side-channel depends on the number of bits switching from one state to the other [6,8] at a given time. A microprocessor is modeled as a state where transitions from state to state are triggered by events such as the edges of a clock signal.This seems relevant when looking at a logical elementary gate as implemented in CMOS technology. The current consumed is related to the energy required to ?ip the bits from one state to the next. It is composed of two main contributions: the capacitor’s charge and the short circuit induced by the gate transition. Curiously,this elementary behavior is commonly admitted but has never given rise to any satisfactory model that is widely applicable. Only hardware designers are familiar with simulation tools to foresee the current consumption of microelectronic devices.

If the transition model is adopted, a basic question is posed: what is the reference state from which the bits are switched? We assume here that this reference state is a constant machine word, R, which is unknown, but not necessarily zero. It will always be the same if the same data manipulation always occurs at the same time, although this assumes the absence of any desyn chronizing effect.Moreover, it is assumed that switching a bit from 0 to 1 or from 1 to 0 requires the same amount of energy and that all the machine bits handled at a given time are perfectly balanced and consume the same.

These restrictive assumptions are quite realistic and affordable without any thorough knowledge of microelectronic devices. They lead to a convenient expression for the leakage model. Indeed the number of ?ipping bits to go from R to D is described by H (D ⊕R) also called the Hamming distance between D and R. This statement encloses the Hamming weight model

b

which assumes that R = 0. If D is a uniform random variable, so is D ⊕R, and H (D ⊕R) has the same mean m/2 and variance m/4 as H (D).

We also assume a linear relationship between the current consumption and H (D ⊕R). This can be seen as a limitation but considering a chip as a large set of elementary electrical components, this linear model ?ts reality quite well. It does not represent the entire consumption of a chip but only the data dependent part. This does not seem unrealistic because the bus lines are usually considered as the most consuming elements within a micro-controller. All the remaining things in the power consumption of a chip are assigned to a term denoted b which is assumed independent from the other variables: encloses of sets, time dependent components and noise. Therefore the basic model for the data dependency can be written:

b R D aH W +⊕=)(

where a is a scalar gain between the Hamming distance and W the power consumed.

3 The Linear Correlation Factor

A linear model implies some relationships between the variances of the different terms

considered as random variables:2222b H W a σσσ+=,Classical statistics introduce the correlation

factor ρWH between the Hamming distance and the measured power to assess the linear model ?tting rate. It is the covariance between both random variables normalized by the product of their standard deviations.Under the uncorrelated noise assumption, this de?nition leads to: 222224)

,cov(b b b H H W WH m a m a a a H W σσσσσσρ+=+==

This equation complies with the well known property: ?1 ≤ ρWH ≤ +1: for a perfect model the correlation factor tends to ±1 if the variance of noise tends to0, the sign depending on the sign of the linear gain a. If the model applies only to l independent bits amongst m, a partial correlation still exists:

m

l m a l

a H WH

b m l W ρσρ=+=22/4

4 Secret Inference Based on Correlation Power Analysis

The relationships written above show that if the model is valid the correlation factor is maximized when the noise variance is minimum. This means that ρWH can help to determine the reference state R. Assume, just like in DPA, that a set of known but randomly varying data D and a set of related power consumption W are available. If the 2m possible values of R are scanned exhaustively they can be ranked by the correlation factor they produce when combined with the observation W . This is not that expensive when considering an 8-bit micro- controller, the case with many of today’s smart cards, as only 256 values are to be tested. On 32-bit architectures this exhaustive search cannot be applied as such. But it is still possible to work with partial correlation or to introduce prior knowledge.

Let R be the true reference and H = H (D ⊕R) the right prediction on the Hamming distance. Let R represent a candidate value and H the related model H = H (D ⊕ R ). Assume a value of R that has k bits that differ from those of R, then: H (R ⊕ R ) = k. Since b is independent from other variables, the correlation test leads to (see [5]):

m

k m H H a H b aH WH HH WH H W H W WH 2)',cov()

',cov(''''-===+=ρρρσσσσρ This formula shows how the correlation factor is capable of rejecting wrong candidates for R. For instance, if a single bit is wrong amongst an 8-bit word, the correlation is reduced by 1/4. If all the bits are wrong, i-e R = ?R, then an anti-correlation should be observed with ρWH = ?ρWH . In absolute value or if the linear gain is assumed positive (a > 0), there cannot be any R leading to a higher correlation rate than R. This proves the uniqueness of the solution and therefore how the reference state can be determined.

This analysis can be performed on the power trace assigned to a piece of code while manipulating known and varying data. If we assume that the handled data is the result of a XOR operation between a secret key word K and a known message word M , D = K ⊕ M , the procedure described above, i-e exhaustive search on R and correlation test, should lead to K ⊕ R as sociated with max(ρWH ). Indeed if a correlation occurs when M is handled with respect to R1 , another has to occur later on, when M ⊕ K is manipulated in turn, possibly with a different reference state R2 (in fact with K ⊕ R2 since only M is known).

For instance, when considering the ?rst Add Round Key function at the beginning of the AES algorithm embedded on an 8-bit processor, it is obvious that such a method leads to the whole key masked by the constant reference byte R2 . If R2 is the same for all the key bytes, which is highly plausible, only 28 possibilities remain to be tested by exhaustive search to infer the entire key material. This complementary brute force may be avoided if R2 is determined by other means or known to be always equal to 0 (on certain chips).

This attack is not restricted to the ⊕ operation. It also applies to many other operators often encountered in secret key cryptography. For instance, other arithmetic, logical operations or look-up tables (LUT) can be treated in the same manner by using H(LUT(M K ) ⊕ R), where represents the involved function i.e. ⊕, +, -, OR, AND, or whatever operation. Let’s notice that the ambiguity between K and K ⊕R is completely removed by the substitution boxes encountered in secret key algorithms thanks to the non-linearity of the corresponding LUT: this may require to exhaust both K and R, but only once for R in most cases. To conduct an analysis in the best conditions, we emphasize the bene?t of correctly modeling the whole machine word that is actually handled and its transition with respect to the reference state R which is to be determined as an unknown of the problem.

5 Estimation

In a real case with a set of N power curves Wi and N associated random data words Mi , for a given reference state R the known data words produce a set of N predicted Hamming distances Hi,R = H (Mi ⊕ R). An estimate ρ?WH of the correlation factor ρWH is given by the following formula:

∑∑∑∑∑∑∑---=2,2

,22,,^)()()(R i R i i i R

i i R i i WH H H N W W N H W H W N R ρ

where the summations are taken over the N samples (i = 1,N ) at each time step within the power traces Wi (t).

It is theoretically diffi cult to compute the variance of the estimator ρ?WH with respect to the number of available samples N . In practice a few hundred experiments suffice to provide a workable estimate of the correlation factor. N has to be increased with the model variance m/4 (higher on a 32-bit architecture) and in presence of measurement noise level obviously. Next results will show that this is more than necessary for conducting reliable tests. The reader is referred to [5] for further discussion about the estimation on experimental data and optimality

issues. It is shown that this approach can be seen as a maximum likelihood model ?tting procedure when R is exhausted to maximize ρ?WH .

6 Experimental Results

This section aims at confronting the leakage model to real experiments. General rules of behavior are derived from the analysis of various chips for secure devices conducted during the passed years.

Our ?rst experience was performed onto a basic XOR algorithm impl emented in a 8-bit chip known for leaking information (more suitable for didactic purpose). The sequence of instructions was simply the following:

–load a byte D1 into the accumulator

–XOR D1 with a constant D2

–store the result from the accumulator to a destination memory cell.

The program was executed 256 times with D1 varying from 0 to 255. As displayed on Figure 1, two signi?cant correlation peaks were obtained with two di fferent reference states: the ?rst one being the address of D1 , the second one the op code of the XOR instruction. These curves bring the experimental evidence of leakage principles that previous works just hint at, without going into more detail [16,8,6,17]. They illustrate the most general case of a transfer sequence on a common bus. The address of a data word is transmitted just before its value that is in turn immediately followed by the op code of the next instruction which is fetched. Such a behavior can be observed on a wide variety of chips even those implementing 16 or 32-bit architectures. Correlation rates ranging from 60% to more than 90% can often be obtained. Figure 2 shows an example of partial correlation on a 32-bit architecture: when only 4 bits are predicted among 32, the correlation loss is in about the ratio √8 which is consistent with the displayed correlations.

This sort of results can be observed on various technologies and implementations. Nevertheless the following restrictions have to be mentioned:

–Sometimes the reference state is systematically 0. This can be assigned to the so-called

charged logic where the bus is cleared between each transferred value. Another possible reason is that complex architectures implement separated for data and addresses, that may prohibit certain transitions. In all those cases the Hamming weight model is recovered as a particular case of the more general Hamming distance model.

–The sequence of correlation peaks may sometimes be blurred or spread over the time in presence of a pipe line.

–Some recent technologies implement hardware security features designed to impede statistical power analysis. These countermeasures offer various level so going from the most naive and easy to bypass, to the most effective which merely cancel any data dependency.

There are different kinds of countermeasures which are completely similar to those designed against DPA.

–Some of them consist in introducing desyn chronization in the execution of the process so that the curves are not aligned anymore within a same acquisition set. For that purpose there exist various techniques such as fake cycles insertion, unstable clocking or random delays [6,18]. In certain cases their effect can be corrected by applying appropriate signal processing.

Fig. 1. Upper: consecutive correlation peaks for two different reference states.

Lower: for varying data (0-255), model array and measurement array taken at the time of the second

correlation peak.

Fig. 2. Two correlation peaks for full word (32 bits) and partial (4 bits) predictions. According to theory

the 20% peak should rather be around 26%.

–Other countermeasures consist in blurring the power traces with additional noise or ?ltering circuitry [19]. Sometimes they can be bypassed by curves selection and/or averaging or by using another side channel such as electromagnetic radiation [9,1].

–The data can also be ciphered dynamically during a process by hardware(such as bus encryption) or software means (data masking with a random[11,7,20,10]), so that the handled variables become unpredictable: then no correlation can be expected anymore. In theory sophisticated attacks such as higher order analysis [15] can overcome the data masking method; but they are easy to thwart in practice by using desyn chronization for instance.

Indeed, if implemented alone, none of these countermeasures can be considered as absolutely secure against statistical analyses. They just increase the amount of effort and level of expertise required to achieve an attack. However combined defenses, implementing at least two of these countermeasures, prove to be very efficient and practically dissuasive. The state of the art of countermeasures in the design of tamper resistant devices has made big advances in the recent years.It is now admitted that security requirements include sound implementations as much as robust cryptographic schemes.

7 Comparison with DPA

This section addresses the comparison of the proposed CPA method with Differential Power Analysis (DPA). It refers to the former works done by Messerges etal. [16,17] who formalized the ideas previously suggested by Kocher [12,13]. A critical study is proposed in [5].

7.1 Practical Proble ms with DPA: The “Ghost Peaks”

We just consider hereafter the practical implementation of DPA against the DES substitutions (1st round). In fact this well-known attack works quite well only if the following assumptions are ful?lled:

1. Word space assumption: within the word hosting the predicted bit, the contribution of the non-targeted bits is independent of the targeted bit va lue. Their average in?uence in the curves pack of 0 is the same as that in the curves pack of 1. So the attacker does not need to care about these bits.

2. Guess space assumption: the predicted value of the targeted bit for any wrong sub-key guess does not depend on the value associated to the correct guess.

3. Time space assumption: the power consumption W does not depend on the value of the targeted bit except when it is explicitly handled.

But when confronted to the experience, the attack comes up against the following facts.

–Fact A. For the correct guess, DPA peaks appear also when the targeted bit is not explicitly handled. This is worth being noticed albeit not really embarrassing. However this contradicts the third assumption.

–Fact B. Some DPA peaks also appear for wrong guesses: they are called ―ghost peaks‖. This fact is more problematic for making a sound decision and comes in contradiction with the second assumption.

–Fact C. The true DPA peak given by the right guess may be smaller than some ghost peaks, and even null or negative! This seems somewhat amazing and quite confusing for an attacker. The reasons must be searched for inside the crudeness of the optimistic ?rst assumption.

红外数据通信技术外文翻译文献

红外数据通信技术外文翻译文献(文档含中英文对照即英文原文和中文翻译) Infrared Remote Control System Abstract Red outside data correspondence the technique be currently within the scope of world drive extensive usage of a kind of wireless conjunction technique, drive numerous hardware and software platform support. Red outside the transceiver product have cost low, small scaled turn, the baud rate be quick, point to point SSL, be free from electromagnetism thousand Raos

etc. characteristics, can realization information at dissimilarity of the product fast, convenience, safely exchange and transmission, at short distance wireless deliver aspect to own very obvious of advantage. Along with red outside the data deliver a technique more and more mature, the cost descend, red outside the transceiver necessarily will get at the short distance communication realm more extensive of application. The purpose that design this system is transmit customer’s operation information with infrared rays for transmit media, then demodulate original signal with receive circuit. It use coding chip to modulate signal and use decoding chip to demodulate signal. The coding chip is PT2262 and decoding chip is PT2272. Both chips are made in Taiwan. Main work principle is that we provide to input the information for the PT2262 with coding keyboard. The input information was coded by PT2262 and loading to high frequent load wave whose frequent is 38 kHz, then modulate infrared transmit dioxide and radiate space outside when it attian enough power. The receive circuit receive the signal and demodulate original information. The original signal was decoded by PT2272, so as to drive some circuit to accomplish customer’s operation demand. Keywords: Infrared dray；Code；Decoding；LM386；Red outside transceiver 1 Introduction 1.1 research the background and significance Infrared Data Communication Technology is the world wide use of a wireless connection technology, by the many hardware and software platforms supported. Is a data through electrical pulses and infrared optical pulse switch between the wireless data transceiver technology.

中英文参考文献格式

中文参考文献格式 参考文献（即引文出处）的类型以单字母方式标识： M——专著，C——论文集，N——报纸文章，J——期刊文章，D——学位论文，R——报告，S——标准，P——专利；对于不属于上述的文献类型，采用字母“Z”标识。 参考文献一律置于文末。其格式为： （一）专著 示例 [1] 张志建.严复思想研究[M]. 桂林：广西师范大学出版社，1989. [2] 马克思恩格斯全集：第1卷[M]. 北京：人民出版社，1956. [3] [英]蔼理士.性心理学[M]. 潘光旦译注.北京：商务印书馆，1997. （二）论文集 示例 [1] 伍蠡甫.西方文论选[C]. 上海：上海译文出版社，1979. [2] 别林斯基.论俄国中篇小说和果戈里君的中篇小说[A]. 伍蠡甫.西方文论选：下册[C]. 上海：上海译文出版社，1979. 凡引专著的页码，加圆括号置于文中序号之后。 （三）报纸文章 示例 [1] 李大伦.经济全球化的重要性[N]. 光明日报，1998-12-27，（3） （四）期刊文章 示例 [1] 郭英德.元明文学史观散论[J]. 北京师范大学学报（社会科学版），1995（3）. （五）学位论文 示例 [1] 刘伟.汉字不同视觉识别方式的理论和实证研究[D]. 北京：北京师范大学心理系，1998. （六）报告 示例 [1] 白秀水，刘敢，任保平. 西安金融、人才、技术三大要素市场培育与发展研究[R]. 西安：陕西师范大学西北经济发展研究中心，1998. （七）、对论文正文中某一特定内容的进一步解释或补充说明性的注释，置于本页地脚，前面用圈码标识。 参考文献的类型 根据GB3469-83《文献类型与文献载体代码》规定，以单字母标识： M——专著（含古籍中的史、志论著） C——论文集 N——报纸文章 J——期刊文章 D——学位论文 R——研究报告 S——标准 P——专利 A——专著、论文集中的析出文献 Z——其他未说明的文献类型 电子文献类型以双字母作为标识： DB——数据库 CP——计算机程序 EB——电子公告

中英文论文对照格式

英文论文APA格式 英文论文一些格式要求与国内期刊有所不同。从学术的角度讲，它更加严谨和科学，并且方便电子系统检索和存档。 版面格式

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零售企业营销策略中英文对照外文翻译文献

零售企业营销策略中英文对照外文翻译文献(文档含英文原文和中文翻译)

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通信工程项目毕业材料外文翻译

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