Goldreich and Krawczyk proved that there do not exist 3-round black-box zero-knowledge proofs or arguments for languages outside BPP. In 1998, Hada and Tanaka used non-standard assumptions to provide a 3-round zero-knowledge argument for every language in NP which was not black-box zero-knowledge. We present a non-black-box simulatable 3-round zero-knowledge proof system for NP, which is secure even when the prover has unbounded computational resources. However, we require a non-standard assumption (similar to those used by Hada and Tanaka) in order to prove our protocol is zero-knowledge. Additionally, we provide a proof of knowledge framework in which to view this type of non-standard assumption. In this thesis, I designed and implemented a compiler which performs optimizations that reduce the number of low-level floating point operations necessary for a specific task; this involves the optimization of chains of floating point operations as well as the implementation of a "fixed" point data type that allows some floating point operations to simulated with integer arithmetic. The source language of the compiler is a subset of C, and the destination language is assembly language for a micro-floating point CPU. An instruction-level simulator of the CPU was written to allow testing of the code. A series of test pieces of codes was compiled, both with and without optimization, to determine how effective these optimizations were.
The decentralisation of educational administration in Indonesia has recently been the focus of much interest throughout the Asia-Pacific region. With its massive population and multi-ethnic social background, Indonesia serves as an interesting example when considering the real impact of decentralized systems on the financing of Junior Secondary Education in developing countries. In this paper, first, the decentralisation scheme and the current financial budgeting system at the school level of junior secondary education in Indonesia are outlined. Second, a quantitative analysis of the unit cost among provinces, districts and schools is detailed. Finally, policy tasks for the further development of decentralised administration will be identified. Even under the former centralised regime, each school or district was operating in a varied way, while it is also true that the process of this diversified financial budgeting was not always a strategic one.
During the last three decades the planning region "Amazônia Legal" with 5 million km2, the world's largest area of tropical forests, endured six phases of regional development programmes with far-reaching state and private activities: 1) National Integration, 2) Polamazônia, 3) Integrated rural development, 4) Grande Carajás, 5) Strategies of sustainable development in the Pilot Programme, 6) Avança Brasil. Consequence of all regional development programmes, with the exception of the Pilot Programme, has been an increasing destruction of tropical forests, comprising 14 % of the total forest area. The new Avança Brasil Mega-Programme, financed mostly by the government, with enormous expansion of infrastructure and complex economic activities, is provoking large environmental impacts, representing a major challenge for the future development of Amazonia. After the abolishment of SUDAM because of fraud and corruption, decentralized regional development will have to satisfy the basic needs of the regional population on the basis of sustainable management of forest resources and the preservation of biodiversity in Amazonia.
This paper compares optimal financial contracts with centralized and decentralized\nfirms. Under centralized contracting headquarters raises funds on behalf of multiple projects and then allocates the funds on the firm’s internal capital market. Under decentralized contracting each project raises funds separately on the external capital market. The benefit of centralization is that headquarters can use excess liquidity from high-cash flow projects to buy continuation rights for low cash-flow projects. This allows headquarters to make greater repayments to investors, which eases financing constraints ex ante. The cost is that headquarters may pool cash flows from several projects, thereby accumulate internal funds, and make follow-up investments without having to return to the capital market. Absent any capital market discipline, however, it is more difficult for investors to force headquarters to pay out funds, which tightens ex-ante financing constraints.
In this tutorial, selected topics of cryptology and of computational complexity theory are presented. We give a brief overview of the history and the foundations of classical cryptography, and then move on to modern public-key cryptography. Particular attention is paid to cryptographic protocols and the problem of constructing the key components of such protocols such as one-way functions. A function is one-way if it is easy to compute, but hard to invert. We discuss the notion of one-way functions both in a cryptographic and in a complexity-theoretic setting. We also consider interactive proof systems and present some interesting zero-knowledge protocols. In a zero-knowledge protocol one party can convince the other party of knowing some secret information without disclosing any bit of this information. Motivated by these protocols, we survey some complexity-theoretic results on interactive proof systems and related complexity classes.
Canetti and Fischlin have recently proposed the security notion <em>universal composability</em> for commitment schemes and provided two examples. This new notion is very strong. It guarantees that security is maintained even when an unbounded number of copies of the scheme are running concurrently, also it guarantees non-malleability, resilience to selective decommitment, and security against adaptive adversaries. Both of their schemes uses Theta(k) bits to commit to one bit and can be based on the existence of trapdoor commitments and non-malleable encryption.<br /> <br />We present new universally composable commitment schemes based on the Paillier cryptosystem and the Okamoto-Uchiyama cryptosystem. The schemes are efficient: to commit to k bits, they use a constant number of modular exponentiations and communicates O(k) bits. Furthermore the scheme can be instantiated in either perfectly hiding or perfectly binding versions. These are the first schemes to show that constant expansion factor, perfect hiding, and perfect binding can be obtained for universally composable commitments.<br /> <br />We also show how the schemes can be applied to do efficient zero-knowledge proofs of knowledge that are universally composable.
We present several new and fairly practical public-key encryption schemes and prove them secure against adaptive chosen ciphertext attack. One scheme is based on Paillier's Decision Composite Residuosity (DCR) assumption, while another is based in the classical Quadratic Residuosity (QR) assumption. The analysis is in the standard cryptographic model, i.e., the security of our schemes does not rely on the Random Oracle model.<br /> <br />We also introduce the notion of a universal hash proof system. Essentially, this is a special kind of non-interactive zero-knowledge proof system for an NP language. We do not show that universal hash proof systems exist for all NP languages, but we do show how to construct very efficient universal hash proof systems for a general class of group-theoretic language membership problems.<br /> <br />Given an efficient universal hash proof system for a language with certain natural cryptographic indistinguishability properties, we show how to construct an efficient public-key encryption schemes secure against adaptive chosen ciphertext attack in the standard model. Our construction only uses the universal hash proof system as a primitive: no other primitives are required, although even more efficient encryption schemes can be obtained by using hash functions with appropriate collision-resistance properties. We show how to construct efficient universal hash proof systems for languages related to the DCR and QR assumptions. From these we get corresponding public-key encryption schemes that are secure under these assumptions. We also show that the Cramer-Shoup encryption scheme (which up until now was the only practical encryption scheme that could be proved secure against adaptive chosen ciphertext attack under a reasonable assumption, namely, the Decision Diffie-Hellman assumption) is also a special case of our general theory.
The term "development finance \n institutions" (DFI) encompasses no only government \n development banks, but also nongovernmental micro-finance \n organizations, that match grants to attempt to promote \n community development, decentralization of power, and local \n empowerment. Measures of the social cost of DFIs that \n receive public funds, help to check whether DFIs are good \n uses of public funds, i.e., if the social benefit of a DFI \n exceeds the social cost, then public funds are indeed \n well-spent, further improving social welfare. This report \n describes the measurement of costs but not of benefits; but \n even without knowledge of benefits, knowledge of costs can \n help to adequately spend funds. Two measures of social cost \n are presented: first, the Subsidy Dependence Index (SDI) - \n the ratio of subsidy received to revenue from loans; and, \n subsidy is the social cost of the public funds used to run a \n DFI - which does not discount flows, rather it works in \n short time frames, or when the rate of time preference is \n low; second, the Net Present Cost to Society (NPCs) - like \n standard present-value measures, it discounts cash flows, \n and works in any time frame. Both SDI and NPCs are tools, to \n help establish benchmarks, chart trends, and compare a DFI \n with identical clients, and services. It is stipulated that \n measurement of the social cost of public DFIs matters \n because funds earmarked for development are scarce, while \n subsidies for DFIs could be adequate, provided social \n welfare improves in a broader scale.
Committee for Proprietary Medicinal Products (CPMP)
A number of recent applications have led to CPMP discussions concerning the interpretation of superiority, noninferiority and equivalence trials. These issues are covered in ICH E9 (Statistical Principles for Clinical Trials). There is further relevant material in the Step 2 draft of ICH E10 (Choice of Control Group) and in the CPMP Note for Guidance on the Investigation of Bioavailability and Bioequivalence. However, the guidelines do not address some specific difficulties that have arisen in practice. In broad terms, these difficulties relate to switching from one design objective to another at the time of analysis. The types of trials in question are those designed to compare a new product with an active comparator. The objective may be to demonstrate: the superiority of the new product the noninferiority of the new product or the equivalence of the two products. When the results of the trial become available, they may suggest an alternative interpretation. Thus the results of a superiority trial may only appear to be sufficient to support noninferiority, while the results of a noninferiority trial may appear to support superiority. Alternatively, the results of an equivalence trial may appear to support a tighter range of equivalence. A satisfactory approach to this subject requires an understanding of confidence intervals and the manner in which they capture the results of the trial and indicate the conclusions that can be drawn from them. Such an understanding also leads to an appreciation of why power calculations are of relatively little interest when a trial is complete. For simplicity, this paper addresses the issues of superiority, noninferiority and equivalence from the perspective of an efficacy trial with a single primary variable. Some comments on other situations are made in Section VI. It is assumed throughout this document that switching the objective of a trial does not lead to any change in the selection or definition of the primary variable. A superiority trial is designed to detect a difference between treatments. The first step of the analysis is usually a test of statistical significance to evaluate whether the results of the trial are consistent with the assumption of there being no difference in the clinical effect of the two treatments. In a trial of good quality, the degree of statistical significance (P value) indicates the probability that the observed difference, or a larger one, could have arisen by chance assuming that no difference really existed. The smaller this probability is, the more implausible is the assumption that there really is no difference between the treatments. Once it is accepted that the assumption of ‘no difference’ is untenable, it then becomes important to estimate the size of the difference in order to assess whether the effect is clinically relevant. This has two aspects. First there is the best estimate of the size of the difference between treatments (point estimate). For normally distributed data this is usually taken as the observed difference between the mean values on each. Next, there is the range of values of the true difference that are plausible in the light of the results of the trial (confidence interval). It is clear that this range should not include zero since the possibility of a zero difference has already been rejected as unreasonable. The method of constructing confidence intervals generally ensures that this is so, provided it corresponds to the choice of significance test. Thus the following two statements are usually equivalent: The two-sided 95% confidence interval for the difference between the means excludes zero. The two means are statistically significantly different at the 5% level (P < 0.05) two-sided. The above text addresses the situation where the difference between two mean values is the statistic of interest and a zero difference represents no effect. In practice a number of other summary statistics are used for the evaluation of differences between treatments, for example the odds ratio for proportions or the ratio of geometric means in bio-equivalence studies. (The latter arises from the logarithmic transformation used for bioavailability data.) In such cases the same principles apply but ‘no difference’ may be represented by a value other than zero – a value of 1 in both the examples quoted here. In these cases it is the position of the confidence interval for the test statistic relative to this ‘no difference’ value that is of interest. When significance tests are carried out in practice, precise numerical values of probabilities are usually quoted, for example P = 0.032, because this is more informative than P < 0.05. This allows judgement to be based more precisely on the extent of the disagreement between the null hypothesis and the observed data rather than on the approximations implied by using cut-off points of 0.05, 0.01 and 0.001. However, confidence intervals have to be associated with a specific probability value (coverage probability) and this is nearly always taken as 95% (0.95). When a difference is statistically significant at a more extreme level, e.g. P = 0.002, the two-sided 95% confidence interval will exclude zero by a wider margin. Figure 1 illustrates these points. Relationship between significance tests and confidence intervals. Whether the observed difference is indeed clinically relevant is a matter of judgement. In contrast to an equivalence or noninferiority trial where clinical relevance is addressed through the prestudy choice of Δ (see II.2 and II.3), in a superiority trial clinical relevance requires separate consideration: a statistically significant difference may not be clinically relevant. The difference taken as the basis of the power calculation in a superiority trial cannot be assumed to provide a suitable value. Note that in Figure 1, and throughout the rest of the document, it is assumed that values to the right of zero correspond to a better response on the new treatment so that values to the left are worse, i.e. better on the control treatment. An equivalence trial is designed to confirm the absence of a meaningful difference between treatments. In this case it is more informative to conduct the analysis by means of the calculation and examination of the confidence interval although there are closely related methods using significance test procedures. (See also II.3.) A margin of clinical equivalence (Δ) is chosen by defining the largest difference that is clinically acceptable, so that a difference bigger than this would matter in practice. There are well-recognized difficulties associated with this task which will not be discussed in any detail here. If the two treatments are to be declared equivalent, then the two-sided 95% confidence interval – which defines the range of plausible differences between the two treatments – should lie entirely within the interval −Δ to + Δ, see Figure 2. There are situations in which the equivalence margins may be chosen asymmetrically with respect to zero. Confidence interval approach to analysis of equivalence trial. In the case of bioequivalence studies a coverage probability of 90% for the confidence interval has become the accepted standard when evaluating whether the average values of the pharmacokinetic parameters of two formulations are sufficiently close. Clinical equivalence trials, with two-sided 95% confidence intervals, may be carried out when conventional bio-equivalence trials are impossible, for example in the case of a generic inhaled or topically applied product. In Phase III drug development, noninferiority trials are more common than equivalence trials. In these we wish to show that a new treatment is no less effective than an existing treatment – it may be more effective or it may have a similar effect. Again a confidence interval approach is the most straightforward way of performing the analysis but now we are only interested in a possible difference in one direction. Hence the two-sided 95% confidence interval should lie entirely to the right of the value −Δ, see Figure 3. Non-inferiority trials are sometimes mistakenly referred to, and designed as, equivalence trials. This distinction is important and can be a source of confusion. Confidence interval approach to analysis of non-inferiority trial. Note also that by using the closely related significance testing procedures referred to in II.2, it is possible to calculate a P value associated with the null hypothesis of inferiority. This is a valuable further aid to assessing the strength of the evidence in favour of noninferiority. It will be assumed throughout this document that two-sided 95% confidence intervals are to be used for all clinical trials whatever their objective. Among other benefits, this preserves consistency between significance testing and subsequent estimation. It is also consistent with the guidance provided in the ICH E9 Note for Guidance. If one-sided intervals are used, then they should be used with a coverage probability of 97.5%. In the special case of bioequivalence studies, two-sided 90% confidence intervals have been established as the norm as recommended, for example, in the CPMP Note for Guidance on the Investigation of Bioavailability and Bioequivalence. A conclusion of equivalence or noninferiority clearly depends upon the value of Δ chosen as the maximum acceptable difference. It is always possible to choose a value of Δ which leads to a conclusion of equivalence or noninferiority if it is chosen after the data have been inspected. Since the choice of Δ is generally a difficult one, there is ample room for bias here, however, well intentioned the researcher may be. Plausible arguments may often be advanced for a retrospective choice. In the design of equivalence and noninferiority trials, this reason (amongst others) makes it necessary for the choice of Δ, and the reasoning behind the choice, to be set down in advance by the researcher in the study protocol. The corresponding coverage probability for the confidence interval (usually 95%) should also be chosen at this time. (See Section IV.2 for how these requirements apply when objectives are changed.) The question of how to choose an appropriate Δ will be addressed in a subsequent CPMP Points to Consider. Pre-definition of a trial as a superiority trial, an equivalence trial or a noninferiority trial is necessary for numerous reasons including the following: to ensure that comparator treatments, doses, patient populations and endpoints are appropriate (see ICH E10) to allow sample size estimates to be based on the correct power calculations to ensure that equivalence and noninferiority criteria are predefined to permit appropriate analysis plans to be described in the protocol to ensure that the trial has sufficient sensitivity to achieve its objectives (see ICH E10) If the objective of a trial is switched from superiority to noninferiority, or vice versa, these aspects may lead to greater difficulty than the interpretation of significance tests and confidence intervals. The only switching which is likely to have any practical relevance is switching between superiority and noninferiority. The place of equivalence trials is so specific that they stand alone. If the 95% confidence interval for the treatment effect not only lies entirely above −Δ but also above zero then there is evidence of superiority in terms of statistical significance at the 5% level (P < 0.05). See Figure 4. In this case it is acceptable to calculate the P value associated with a test of superiority and to evaluate whether this is sufficiently small to reject convincingly the hypothesis of no difference. There is no multiplicity argument that affects this interpretation because, in statistical terms, it corresponds to a simple closed test procedure. Usually this demonstration of a benefit is sufficient on its own, provided the safety profiles of the new agent and the comparator are similar. When there is an increase in adverse events, however, it is important to estimate the size of the effect to evaluate whether it is sufficient in clinical terms to outweigh the adverse effects. Non-inferiority to superiority. There are a number of other factors that might be affected by this changed objective. If the comparator was suitable for a demonstration of noninferiority, then there should be well-controlled data to show that it is an effective treatment. Hence, for proof of efficacy, a clear demonstration of superiority to the comparator in terms of statistical significance should be acceptable. Non-inferiority trials are generally large because of their need to exclude the possibility of a small degree of inferiority of a new agent relative to an active control. However if the new agent is actually superior to control by a small amount, then the power to show its noninferiority is increased. Demonstrating the small amount of superiority to control might in principle require the planning of an even larger trial. When the trial is completed, however, the results provided by the confidence interval supply a concrete assessment of the precision actually achieved, superseding any calculations of power carried out before the trial was undertaken. Since the comparator in a noninferiority trial must be an effective agent, any superiority to that agent should carry the implication of acceptable superiority to no treatment (placebo). For this reason the size of the additional clinical benefit demonstrated is not likely to be relevant to a claim of efficacy except in relation to any increase in adverse effects and hence relative risk/benefit. However, when the proposed licence includes a claim of superiority to the comparator, the size of the additional benefit should be discussed in clinical terms. In a superiority trial the full analysis set, based on the ITT (intention-to-treat) principle, is the analysis set of choice, with appropriate support provided by the PP (per protocol) analysis set. In a noninferiority trial, the full analysis set and the PP analysis set have equal importance and their use should lead to similar conclusions for a robust interpretation. A switch of objective would require this difference of emphasis to be recognized. More details of the relative importance of these two analysis sets in superiority and noninferiority trials can be found in the ICH E9 Note for guidance. A trial to show equivalence or noninferiority must show a high degree of consistency with protocolled plans if it is to be reliable. Deviations from the inclusion criteria, from the intended treatment regimen, from the schedule, manner and precision of taking measurements, and so on, all tend to reduce the sensitivity of a trial and to make a conclusion of ‘no difference’ more likely, even when the deviations are of an unsystematic or random nature. The size of the bias associated with these and other departures from the protocol is generally unknown and may render such a trial uninterpretable. Failure to show a difference between two treatments can also arise when both treatments are inefficacious, perhaps as a result of being inappropriately administered. This problem does not affect superiority trials to the same extent because the demonstration of a difference is itself validation of the sensitivity of the trial. The estimate of the size of the effect may however, be similarly affected. For these reasons, switching from noninferiority to superiority is likely to carry with it a greater degree of confidence in the conclusion. Switching the objective of a trial from noninferiority to superiority is feasible provided: The trial has been properly designed and carried out in accordance with the strict requirements of a noninferiority trial. Actual P values for superiority are presented to allow independent assessment of the strength of the evidence. Analysis according to the intention-to-treat principle is given greatest emphasis. If a superiority trial fails to detect a significant difference between treatments, there may be interest in the lesser objective of establishing noninferiority. If the results of the superiority trial are summarized by means of a 95% confidence interval for the treatment difference, the lower end of that confidence interval provides a quantitative estimate of the minimum estimated effect of the new treatment relative to the comparator. When the study protocol an acceptable, margin −Δ for noninferiority, the objective less a noninferiority margin would appear only to make in trials with noninferiority as an However, in any superiority trial where noninferiority may be an acceptable for it is to a noninferiority margin in the protocol in order to the difficulties that can arise from such it is also to design to the possible need to that the study sufficient sensitivity to detect the drug effects of interest (see It is important to that there are of where noninferiority to an active control is to be acceptable as the or evidence of efficacy, and trials are to In trials where there is no noninferiority such a has to be after the and in situations this will not be It is likely that the will have to be after the results have been and there may be little basis for an objective choice of margin. there does not appear to be a statistical multiplicity related to this switch of that does not the difficulties associated with the definition of A number of other issues require A comparator chosen for a demonstration of superiority may not be acceptable for a conclusion of noninferiority. In order for it to be acceptable, it will be necessary to that there are data from good superiority trials consistent evidence that the comparator is an effective treatment with and establishing the size of its effect relative to no treatment. There should also be a basis for that the same degree of efficacy would be in the trial. For example, the patient and the endpoints should be similar. These issues are covered in ICH in the results provided by the confidence interval supply a concrete assessment of the precision actually by a clinical trial, superseding any calculations of power carried out before the trial was undertaken. The position of the lower end of the confidence interval relative to the of noninferiority provides the for noninferiority. In a superiority trial the full analysis set, based on the ITT (intention-to-treat) principle, is the analysis set of choice, with appropriate support provided by the PP (per protocol) analysis set. In a noninferiority trial the full analysis set and the PP analysis set have equal importance and their use should lead to similar conclusions for a robust interpretation. A switch of objective would require this difference of emphasis to be recognized. More details of the relative importance of these two analysis sets in superiority and noninferiority trials can be found in the ICH E9 Note for Guidance. A trial to show equivalence or noninferiority must show a high degree of consistency with protocolled plans if it is to be reliable. Deviations from the inclusion criteria, from the intended treatment regimen, from the schedule, manner and precision of taking measurements, and so on, all tend to reduce the sensitivity of a trial and to make a conclusion of ‘no difference’ more likely, even when the deviations are of an unsystematic or random nature. The size of the bias associated with these and other departures from the protocol is generally unknown and may render such a trial uninterpretable. Failure to show a difference between two treatments can also arise when both treatments are inefficacious, perhaps as a result of being inappropriately administered. This problem does not affect superiority trials to the same extent because the demonstration of a difference is itself validation of the sensitivity of the trial. For these reasons, switching from superiority to noninferiority is likely to carry with it a lesser degree of confidence in the It will be necessary to to the sensitivity of the trial by or that the control treatment is its efficacy the trial with trials which demonstrated the efficacy of the control agent in of and of of and data that are at to those in the trials similar results from the full analysis set and PP analysis set. Switching the objective of a trial from superiority to noninferiority may be feasible provided: The noninferiority margin with respect to the control treatment was predefined or can be (The latter is likely to difficult and to be to cases where there is a accepted value for Analysis according to the intention-to-treat principle and PP confidence intervals and P values for the null hypothesis of similar The trial was properly designed and carried out in accordance with the strict requirements of a noninferiority trial (see ICH E9 and The sensitivity of the trial is high to ensure that it is of relevant differences if they There is or evidence that the control treatment is its level of A further related that has arisen in with equivalence and noninferiority trials to the equivalence margins when the trial is complete. that a bioequivalence trial a 90% confidence interval for the relative bioavailability of a new that from to we only that the relative bioavailability lies between the conventional of and because these the predefined equivalence can we that it lies between and The interval based on the data is the appropriate one to Hence, if the changed to this study would have satisfactory There is no question of a selection However, if the trial in a confidence interval from to then a change of equivalence margins to would not be acceptable because of the conclusion that the equivalence margin was chosen to the These apply to the 95% confidence intervals used for clinical equivalence and for noninferiority. The confidence interval based on the results of the trial is always the best summary of the It is the choice of equivalence margin that is subject to This should be chosen on the basis of and not chosen to the This Points to has been from the perspective of an efficacy trial active with a single primary variable. In practice some studies have more than one primary and most studies have respect to switching of these requires separate in the of the specific drug development, separate conclusions superiority or noninferiority for in judgement whether the trial as a has established the superiority or noninferiority of the new treatment will upon the requirements for that clinical and the of results all relevant The covered in these Points to can also be applied to specific safety when these have been as endpoints of a trial to compare active In practice the of switching objectives is not relevant to trials, even where noninferiority to is a valuable i.e. for safety The problem of switching objectives can be by a trial in the that both noninferiority and superiority are of value. In this case all the issues in this document should be addressed In the statistical analysis should be using an appropriate from noninferiority to superiority. The interpretation of superiority trials as noninferiority trials and vice is best by the results as a confidence interval for the difference between the test treatment and control. There is no problem associated with the use of this confidence interval as a basis for of interpretation. For a and trial, there are difficulties with the change from noninferiority to superiority that cannot be addressed by appropriate analysis. However, there are more difficulties associated with the switch from superiority to noninferiority because of the possible need to a basis and on, a margin of equivalence after the and because of the difficulties of noninferiority trials. There are for the design of a superiority trial in which noninferiority might be an acceptable When the results with respect to alternative of the equivalence margins the problem from to switch to wider acceptable that equivalence margins may be in this
Open access
Statistical Methods in Clinical Trials
Health Systems, Economic Evaluations, Quality of Life
The Act No. 221999 on regional government and No. 251999 on the balance of finance between central government and regional government, have vast impact on all aspects of the nation life. The impacts are not only on politic and economic aspects, but they also have logic consequence on the changes of Indonesian education form and system. Based on the rules above, the education process in schools should be independent gradually and will not depend on central government moreover on regional government especially in finance. Consequenly, the quality of school must be improved either in planning or process or outcome aspect. To reach the aimed quality of the schools need a management system that can improve the quality of schools. In this frame, this writing offers the total quality management system as a medium to autonomous education having a certain quality in decentralization era.
In this chapter we discuss pseudorandom generators. Loosely speaking, these are efficient deterministic programs that expand short, randomly selected seeds into much longer “pseudorandom” bit sequences (see illustration in Figure 3.1). Pseudorandom sequences are defined as computationally indistinguishable from truly random sequences by efficient algorithms. Hence the notion of computational indistinguishability (i.e., indistinguishability by efficient procedures) plays a pivotal role in our discussion. Furthermore, the notion of computational indistinguishability plays a key role also in subsequent chapters, in particular in the discussions of secure encryption, zero-knowledge proofs, and cryptographic protocols. The theory of pseudorandomness is also applied to functions, resulting in the notion of pseudorandom functions, which is a useful tool for many cryptographic applications. In addition to definitions of pseudorandom distributions, pseudorandom generators, and pseudorandom functions, this chapter contains constructions of pseudorandom generators (and pseudorandom functions) based on various types of one-way functions. In particular, very simple and efficient pseudorandom generators are constructed based on the existence of one-way permutations. We highlight the hybrid technique , which plays a central role in many of the proofs. (For the first use and further discussion of this technique, see Section 3.2.3.) Organization . Basic discussions, definitions, and constructions of pseudorandom generators appear in Sections 3.1–3.4: We start with a motivating discussion (Section 3.1), proceed with a general definition of computational indistinguishability (Section 3.2) next present and discuss definitions of pseudorandom generators (Section 3.3), and finally present some simple constructions (Section 3.4). More general constructions are discussed in Section 3.5.
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Olivier Baudron, Pierre-Alain Fouque, David Pointcheval, Jacques Stern · 5 authors
The aim of electronic voting schemes is to provide a set of protocols that allow voters to cast ballots while a group of authorities collect the votes and output the final tally. In this paper we describe a practical multi-candidate election scheme that guarantees privacy of voters, public verifiability, and robustness against a coalition of malicious authorities. Furthermore, we address the problem of receipt-freeness and incoercibility of voters. Our new scheme is based on the Paillier cryptosystem and on some related zero-knowledge proof techniques. The voting schemes are very practical and can be efficiently implemented in a real system.
A proof is concurrent zero-knowledge if it remains zero-knowledge when many copies of the proof are run in an asynchronous environment, such as the Internet. It is known that zero-knowledge is not necessarily preserved in such an environment. Designing concurrent zero-knowledge proofs is a fundamental issue in the study of zero-knowledge since known zero-knowledge protocols cannot be run in a realistic modern computing environment. In this paper we present a concurrent zero-knowledge proof systems for all languages in NP. Currently, the proof system we present is the only known proof system that retains the zero-knowledge property when copies of the proof are allowed to run in an asynchronous environment. Our proof system has $\tilde{O}(\log^2 k)$ rounds (for a security parameter $k$), which is almost optimal, as it is shown by Canetti Kilian Petrank and Rosen that black-box concurrent zero-knowledge requires $\tildeΩ(\log k)$ rounds. Canetti, Goldreich, Goldwasser and Micali introduced the notion of {\em resettable} zero-knowledge, and modified an earlier version of our proof system to obtain the first resettable zero-knowledge proof system. This protocol requires $k^{θ(1)}$ rounds. We note that their technique also applies to our current proof system, yielding a resettable zero-knowledge proof for NP with $\tilde{O}(\log^2 k)$ rounds.
We consider zero knowledge interactive proofs in a richer, more realistic communication environment. In this setting, one may simultaneously engage in many interactive proofs, and these proofs may take place in an asynchronous fashion. It is known that zero-knowledge is not necessarily preserved in such an environment; we show that for a large class of protocols, it cannot be preserved. Any 4 round (computational) zero-knowledge interactive proof (or argument) for a non-trivial language L is not black-box simulatable in the asynchronous setting.
This paper studies the issue of designing an optimal organizational form: design for sub-units' task allocation, decision-making structure, and incentive schemes for organizational members. Depending on the way tasks are allocated between the sub-units, and whether decision-making is centralized or not, organizations face a trade-off between coordination and information. Task allocation by production processes calls for coordination more strongly than the allocation by final products. Centralized decision-making serves for better coordination, whereas decentralization serves for better information. The coordinational benefit under centralization gets bigger as the organization's common uncertainty increases, and this benefit is magnified when the sub-units are functionally divided by production processes. The informational benefit under decentralization gets bigger as the organization's local uncertainty increases, and this benefit is magnified when the sub-units are designed autonomous. Thus, complementarily designed organizations tend to have centralized decision-making structures and fixed salary scheme, whereas less complementarily designed organizations tend to have decentralized decision-making and 'pay for performance' incentive contract.
Teresa García-Milá, Timothy J. Goodspeed, Therese J. McGuire
As part of a process of democratization, many countries spanning Europe, Latin Amertica, Africa, and Asia are reorganizing their governments bydevolving fiscal responsibility and authority to newly empowered regionaland local governments. Although decentralization in each country proceedsdifferently, a common element tends to be an initially heavy relianceon central government grants to fund regional spending. We develop atheoretical model of regional borrowing decisions in which the incentivesfor regional borrowing depend crucially on how the regions expect thefederal system of finance to evolve. We examine the implications of themodel using data on Spanish regions for the period 1984-1995 and findevidence that regions may be borrowing inefficiently in response toincentives imbedded in the Spanish system of fiscal decentralization.
This article studies the effects of political institutions on inflation. In our view, hyperinflation is the manifestation of a tragedy of commons in a divided society with a weak central monetary authority. Economies with fiat money are inherently inflation-prone: the collection of seigniorage through the inflation tax is less conspicuous than other taxes, and the printing of money is essentially costless. In many countries, the control of the money supply is de facto or de jure decentralized. Sets of agents (in various regions or interest groups) can effectively pressure the central government to finance their expenditures. As these interest groups pursue their self-interest, they neglect the welfare effects of the inflation tax on individuals in other groups. These elements combine to imply that countries which rely on the inflation tax to meet the resource demands of competing interest groups will typically experience inefficiently (due to negative spillovers) high inflation.
We model the links between skills and changes in work organization. As the proportion of skilled workers increases, the economy travels through a sequence of organizational equilibria. We show that as the relative supply of skills increases the organization of work becomes more decentralized. Both skilled and unskilled workers become more autonomous and perform a wider range of tasks: decentralization spreads across firms at the expense of the old centralized organization based on a strict division of labor. Moreover, as firms switch to decentralization, their employment structure becomes more homogeneous and wage inequality stops decreasing. These predictions are compared with empirical evidence based on French establishment‐level data and we find support for both of them. This suggests that the long‐term increase in the skill level of the workforce may have been one important factor driving the recent introduction of new work practices by a large number of firms.
The paper provides information and analysis in support of the Bank's subnational development strategy (GN-2026). The strategy will guide future Bank operations in support of decentralization to address the complex issues countries are facing in their efforts to promote the socioeconomic development in subnational territories. The document is divided in three sections: the first section provides an overview of the decentralization process in Latin America and the Caribbean and sets the stage for an analysis of the key isuues that need to be addresses by the Bank when supporting the decentralization process in the region; the next chapter discusses the challenges confronted by subnational governments. The development of subnational governments is key to improving well-being. The next section focuses on the structure of intergovernmental relations and concludes that a well-designed structure provides subnational governments with incentives to efficiently allocate resources to the most socially profitable uses. The next chapter discusses the governance issues involved in decentralization, while the next section concentrates on the institucional aspects of the institucional aspects of decentralization and concludes that the Bank should emphasize the need for subnational governments to have sufficient institutional capacity at socially acceptable levels of performance. Finally, the last chapter discusses the variety of issues related to financing subnational governments and recommends that the Bank stress the paramount importance of sufficient sources of financing for ensuing good subnational government performance.