On Round-Efficient Argument Systems
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Arxiv:2104.04742V2 [Quant-Ph] 13 Apr 2021 Keywords: Quantum Cryptography, Remote State Preparation, Zero-Knowledge, Learning with Errors Table of Contents
Non-Destructive Zero-Knowledge Proofs on Quantum States, and Multi-Party Generation of Authorized Hidden GHZ States Léo Colisson 1, Frédéric Grosshans 1, Elham Kashefi1,2 1 Laboratoire d’Informatique de Paris 6 (LIP6), Sorbonne Université, 4 Place Jussieu, 75252 Paris CEDEX 05, France {leo.colisson, frederic.grosshans}@lip6.fr 2 School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK Abstract. Due to the special no-cloning principle, quantum states appear to be very useful in cryptography. But this very same property also has drawbacks: when receiving a quantum state, it is nearly impossible for the receiver to efficiently check non-trivial properties on that state without destroying it. This allows a malicious sender to send maliciously crafted states without being detected. The natural (destructive) method for testing a quantum state is the “cut-and-choose” method. However, this method has many drawbacks: the security is only linear, and the class of states and properties that can be tested is quite restricted. In this work, we propose a different approach, and we initiate the study of Non-Destructive Zero-Knowledge Proofs on Quantum States. Our method binds a quantum state to a classical encryption of that quantum state. That way, the receiver can obtain guarantees on the quantum state by asking to the sender to prove properties directly on the classical encryption. This method is therefore non-destructive, and it is possible to verify a very large class of properties. For instance, we can force the sender to send different categories of states depending on whether they know a classical password or not. -
CS601 DTIME and DSPACE Lecture 5 Time and Space Functions: T, S
CS601 DTIME and DSPACE Lecture 5 Time and Space functions: t, s : N → N+ Definition 5.1 A set A ⊆ U is in DTIME[t(n)] iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. A = L(M) ≡ w ∈ U M(w)=1 , and 2. ∀w ∈ U, M(w) halts within c · t(|w|) steps. Definition 5.2 A set A ⊆ U is in DSPACE[s(n)] iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. A = L(M), and 2. ∀w ∈ U, M(w) uses at most c · s(|w|) work-tape cells. (Input tape is “read-only” and not counted as space used.) Example: PALINDROMES ∈ DTIME[n], DSPACE[n]. In fact, PALINDROMES ∈ DSPACE[log n]. [Exercise] 1 CS601 F(DTIME) and F(DSPACE) Lecture 5 Definition 5.3 f : U → U is in F (DTIME[t(n)]) iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. f = M(·); 2. ∀w ∈ U, M(w) halts within c · t(|w|) steps; 3. |f(w)|≤|w|O(1), i.e., f is polynomially bounded. Definition 5.4 f : U → U is in F (DSPACE[s(n)]) iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. f = M(·); 2. ∀w ∈ U, M(w) uses at most c · s(|w|) work-tape cells; 3. |f(w)|≤|w|O(1), i.e., f is polynomially bounded. (Input tape is “read-only”; Output tape is “write-only”. -
On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs*
On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs* Benny Applebaum† Eyal Golombek* Abstract We study the randomness complexity of interactive proofs and zero-knowledge proofs. In particular, we ask whether it is possible to reduce the randomness complexity, R, of the verifier to be comparable with the number of bits, CV , that the verifier sends during the interaction. We show that such randomness sparsification is possible in several settings. Specifically, unconditional sparsification can be obtained in the non-uniform setting (where the verifier is modelled as a circuit), and in the uniform setting where the parties have access to a (reusable) common-random-string (CRS). We further show that constant-round uniform protocols can be sparsified without a CRS under a plausible worst-case complexity-theoretic assumption that was used previously in the context of derandomization. All the above sparsification results preserve statistical-zero knowledge provided that this property holds against a cheating verifier. We further show that randomness sparsification can be applied to honest-verifier statistical zero-knowledge (HVSZK) proofs at the expense of increasing the communica- tion from the prover by R−F bits, or, in the case of honest-verifier perfect zero-knowledge (HVPZK) by slowing down the simulation by a factor of 2R−F . Here F is a new measure of accessible bit complexity of an HVZK proof system that ranges from 0 to R, where a maximal grade of R is achieved when zero- knowledge holds against a “semi-malicious” verifier that maliciously selects its random tape and then plays honestly. -
The Complexity of Space Bounded Interactive Proof Systems
The Complexity of Space Bounded Interactive Proof Systems ANNE CONDON Computer Science Department, University of Wisconsin-Madison 1 INTRODUCTION Some of the most exciting developments in complexity theory in recent years concern the complexity of interactive proof systems, defined by Goldwasser, Micali and Rackoff (1985) and independently by Babai (1985). In this paper, we survey results on the complexity of space bounded interactive proof systems and their applications. An early motivation for the study of interactive proof systems was to extend the notion of NP as the class of problems with efficient \proofs of membership". Informally, a prover can convince a verifier in polynomial time that a string is in an NP language, by presenting a witness of that fact to the verifier. Suppose that the power of the verifier is extended so that it can flip coins and can interact with the prover during the course of a proof. In this way, a verifier can gather statistical evidence that an input is in a language. As we will see, the interactive proof system model precisely captures this in- teraction between a prover P and a verifier V . In the model, the computation of V is probabilistic, but is typically restricted in time or space. A language is accepted by the interactive proof system if, for all inputs in the language, V accepts with high probability, based on the communication with the \honest" prover P . However, on inputs not in the language, V rejects with high prob- ability, even when communicating with a \dishonest" prover. In the general model, V can keep its coin flips secret from the prover. -
Lecture 14 (Feb 28): Probabilistically Checkable Proofs (PCP) 14.1 Motivation: Some Problems and Their Approximation Algorithms
CMPUT 675: Computational Complexity Theory Winter 2019 Lecture 14 (Feb 28): Probabilistically Checkable Proofs (PCP) Lecturer: Zachary Friggstad Scribe: Haozhou Pang 14.1 Motivation: Some problems and their approximation algorithms Many optimization problems are NP-hard, and therefore it is unlikely to efficiently find optimal solutions. However, in some situations, finding a provably good-enough solution (approximation of the optimal) is also acceptable. We say an algorithm is an α-approximation algorithm for an optimization problem iff for every instance of the problem it can find a solution whose cost is a factor of α of the optimum solution cost. Here are some selected problems and their approximation algorithms: Example: Min Vertex Cover is the problem that given a graph G = (V; E), the goal is to find a vertex cover C ⊆ V of minimum size. The following algorithm gives a 2-approximation for Min Vertex Cover: Algorithm 1 Min Vertex Cover Algorithm Input: Graph G = (V; E) Output: a vertex cover C of G. C ; while some edge (u; v) has u; v2 = C do C C [ fu; vg end while return C Claim 1 Let C∗ be an optimal solution, the returned solution C satisfies jCj ≤ 2jC∗j. Proof. Let M be the edges considered by the algorithm in the loop, we have jCj = 2jMj. Also, jC∗j covers M and no two edges in M share an endpoint (M is a matching), so jC∗j ≥ jMj. Therefore, jCj = 2jMj ≤ 2jC∗j. Example: Max SAT is the problem that given a CNF formula, the goal is to find a assignment that satisfies as many clauses as possible. -
Interactive Proofs
Interactive proofs April 12, 2014 [72] L´aszl´oBabai. Trading group theory for randomness. In Proc. 17th STOC, pages 421{429. ACM Press, 1985. doi:10.1145/22145.22192. [89] L´aszl´oBabai and Shlomo Moran. Arthur-Merlin games: A randomized proof system and a hierarchy of complexity classes. J. Comput. System Sci., 36(2):254{276, 1988. doi:10.1016/0022-0000(88)90028-1. [99] L´aszl´oBabai, Lance Fortnow, and Carsten Lund. Nondeterministic ex- ponential time has two-prover interactive protocols. In Proc. 31st FOCS, pages 16{25. IEEE Comp. Soc. Press, 1990. doi:10.1109/FSCS.1990.89520. See item 1991.108. [108] L´aszl´oBabai, Lance Fortnow, and Carsten Lund. Nondeterministic expo- nential time has two-prover interactive protocols. Comput. Complexity, 1 (1):3{40, 1991. doi:10.1007/BF01200056. Full version of 1990.99. [136] Sanjeev Arora, L´aszl´oBabai, Jacques Stern, and Z. (Elizabeth) Sweedyk. The hardness of approximate optima in lattices, codes, and systems of linear equations. In Proc. 34th FOCS, pages 724{733, Palo Alto CA, 1993. IEEE Comp. Soc. Press. doi:10.1109/SFCS.1993.366815. Conference version of item 1997:160. [160] Sanjeev Arora, L´aszl´oBabai, Jacques Stern, and Z. (Elizabeth) Sweedyk. The hardness of approximate optima in lattices, codes, and systems of linear equations. J. Comput. System Sci., 54(2):317{331, 1997. doi:10.1006/jcss.1997.1472. Full version of 1993.136. [111] L´aszl´oBabai, Lance Fortnow, Noam Nisan, and Avi Wigderson. BPP has subexponential time simulations unless EXPTIME has publishable proofs. In Proc. -
Probabilistic Proof Systems: a Primer
Probabilistic Proof Systems: A Primer Oded Goldreich Department of Computer Science and Applied Mathematics Weizmann Institute of Science, Rehovot, Israel. June 30, 2008 Contents Preface 1 Conventions and Organization 3 1 Interactive Proof Systems 4 1.1 Motivation and Perspective ::::::::::::::::::::::: 4 1.1.1 A static object versus an interactive process :::::::::: 5 1.1.2 Prover and Veri¯er :::::::::::::::::::::::: 6 1.1.3 Completeness and Soundness :::::::::::::::::: 6 1.2 De¯nition ::::::::::::::::::::::::::::::::: 7 1.3 The Power of Interactive Proofs ::::::::::::::::::::: 9 1.3.1 A simple example :::::::::::::::::::::::: 9 1.3.2 The full power of interactive proofs ::::::::::::::: 11 1.4 Variants and ¯ner structure: an overview ::::::::::::::: 16 1.4.1 Arthur-Merlin games a.k.a public-coin proof systems ::::: 16 1.4.2 Interactive proof systems with two-sided error ::::::::: 16 1.4.3 A hierarchy of interactive proof systems :::::::::::: 17 1.4.4 Something completely di®erent ::::::::::::::::: 18 1.5 On computationally bounded provers: an overview :::::::::: 18 1.5.1 How powerful should the prover be? :::::::::::::: 19 1.5.2 Computational Soundness :::::::::::::::::::: 20 2 Zero-Knowledge Proof Systems 22 2.1 De¯nitional Issues :::::::::::::::::::::::::::: 23 2.1.1 A wider perspective: the simulation paradigm ::::::::: 23 2.1.2 The basic de¯nitions ::::::::::::::::::::::: 24 2.2 The Power of Zero-Knowledge :::::::::::::::::::::: 26 2.2.1 A simple example :::::::::::::::::::::::: 26 2.2.2 The full power of zero-knowledge proofs :::::::::::: -
Introduction to Complexity Theory Big O Notation Review Linear Function: R(N)=O(N)
GS019 - Lecture 1 on Complexity Theory Jarod Alper (jalper) Introduction to Complexity Theory Big O Notation Review Linear function: r(n)=O(n). Polynomial function: r(n)=2O(1) Exponential function: r(n)=2nO(1) Logarithmic function: r(n)=O(log n) Poly-log function: r(n)=logO(1) n Definition 1 (TIME) Let t : . Define the time complexity class, TIME(t(n)) to be ℵ−→ℵ TIME(t(n)) = L DTM M which decides L in time O(t(n)) . { |∃ } Definition 2 (NTIME) Let t : . Define the time complexity class, NTIME(t(n)) to be ℵ−→ℵ NTIME(t(n)) = L NDTM M which decides L in time O(t(n)) . { |∃ } Example 1 Consider the language A = 0k1k k 0 . { | ≥ } Is A TIME(n2)? Is A ∈ TIME(n log n)? Is A ∈ TIME(n)? Could∈ we potentially place A in a smaller complexity class if we consider other computational models? Theorem 1 If t(n) n, then every t(n) time multitape Turing machine has an equivalent O(t2(n)) time single-tape turing≥ machine. Proof: see Theorem 7:8 in Sipser (pg. 232) Theorem 2 If t(n) n, then every t(n) time RAM machine has an equivalent O(t3(n)) time multi-tape turing machine.≥ Proof: optional exercise Conclusion: Linear time is model specific; polynomical time is model indepedent. Definition 3 (The Class P ) k P = [ TIME(n ) k Definition 4 (The Class NP) GS019 - Lecture 1 on Complexity Theory Jarod Alper (jalper) k NP = [ NTIME(n ) k Equivalent Definition of NP NP = L L has a polynomial time verifier . -
Is It Easier to Prove Theorems That Are Guaranteed to Be True?
Is it Easier to Prove Theorems that are Guaranteed to be True? Rafael Pass∗ Muthuramakrishnan Venkitasubramaniamy Cornell Tech University of Rochester [email protected] [email protected] April 15, 2020 Abstract Consider the following two fundamental open problems in complexity theory: • Does a hard-on-average language in NP imply the existence of one-way functions? • Does a hard-on-average language in NP imply a hard-on-average problem in TFNP (i.e., the class of total NP search problem)? Our main result is that the answer to (at least) one of these questions is yes. Both one-way functions and problems in TFNP can be interpreted as promise-true distri- butional NP search problems|namely, distributional search problems where the sampler only samples true statements. As a direct corollary of the above result, we thus get that the existence of a hard-on-average distributional NP search problem implies a hard-on-average promise-true distributional NP search problem. In other words, It is no easier to find witnesses (a.k.a. proofs) for efficiently-sampled statements (theorems) that are guaranteed to be true. This result follows from a more general study of interactive puzzles|a generalization of average-case hardness in NP|and in particular, a novel round-collapse theorem for computationally- sound protocols, analogous to Babai-Moran's celebrated round-collapse theorem for information- theoretically sound protocols. As another consequence of this treatment, we show that the existence of O(1)-round public-coin non-trivial arguments (i.e., argument systems that are not proofs) imply the existence of a hard-on-average problem in NP=poly. -
Circuit Lower Bounds for Merlin-Arthur Classes
Electronic Colloquium on Computational Complexity, Report No. 5 (2007) Circuit Lower Bounds for Merlin-Arthur Classes Rahul Santhanam Simon Fraser University [email protected] January 16, 2007 Abstract We show that for each k > 0, MA/1 (MA with 1 bit of advice) doesn’t have circuits of size nk. This implies the first superlinear circuit lower bounds for the promise versions of the classes MA AM ZPPNP , and k . We extend our main result in several ways. For each k, we give an explicit language in (MA ∩ coMA)/1 which doesn’t have circuits of size nk. We also adapt our lower bound to the average-case setting, i.e., we show that MA/1 cannot be solved on more than 1/2+1/nk fraction of inputs of length n by circuits of size nk. Furthermore, we prove that MA does not have arithmetic circuits of size nk for any k. As a corollary to our main result, we obtain that derandomization of MA with O(1) advice implies the existence of pseudo-random generators computable using O(1) bits of advice. 1 Introduction Proving circuit lower bounds within uniform complexity classes is one of the most fundamental and challenging tasks in complexity theory. Apart from clarifying our understanding of the power of non-uniformity, circuit lower bounds have direct relevance to some longstanding open questions. Proving super-polynomial circuit lower bounds for NP would separate P from NP. The weaker result that for each k there is a language in NP which doesn’t have circuits of size nk would separate BPP from NEXP, thus answering an important question in the theory of derandomization. -
Introduction to Complexity Classes
Introduction to Complexity Classes Marcin Sydow Introduction to Complexity Classes Marcin Sydow Introduction Denition to Complexity Classes TIME(f(n)) TIME(f(n)) denotes the set of languages decided by Marcin deterministic TM of TIME complexity f(n) Sydow Denition SPACE(f(n)) denotes the set of languages decided by deterministic TM of SPACE complexity f(n) Denition NTIME(f(n)) denotes the set of languages decided by non-deterministic TM of TIME complexity f(n) Denition NSPACE(f(n)) denotes the set of languages decided by non-deterministic TM of SPACE complexity f(n) Linear Speedup Theorem Introduction to Complexity Classes Marcin Sydow Theorem If L is recognised by machine M in time complexity f(n) then it can be recognised by a machine M' in time complexity f 0(n) = f (n) + (1 + )n, where > 0. Blum's theorem Introduction to Complexity Classes Marcin Sydow There exists a language for which there is no fastest algorithm! (Blum - a Turing Award laureate, 1995) Theorem There exists a language L such that if it is accepted by TM of time complexity f(n) then it is also accepted by some TM in time complexity log(f (n)). Basic complexity classes Introduction to Complexity Classes Marcin (the functions are asymptotic) Sydow P S TIME nj , the class of languages decided in = j>0 ( ) deterministic polynomial time NP S NTIME nj , the class of languages decided in = j>0 ( ) non-deterministic polynomial time EXP S TIME 2nj , the class of languages decided in = j>0 ( ) deterministic exponential time NEXP S NTIME 2nj , the class of languages decided -
Computational Complexity
Computational complexity Plan Complexity of a computation. Complexity classes DTIME(T (n)). Relations between complexity classes. Complete problems. Domino problems. NP-completeness. Complete problems for other classes Alternating machines. – p.1/17 Complexity of a computation A machine M is T (n) time bounded if for every n and word w of size n, every computation of M has length at most T (n). A machine M is S(n) space bounded if for every n and word w of size n, every computation of M uses at most S(n) cells of the working tape. Fact: If M is time or space bounded then L(M) is recursive. If L is recursive then there is a time and space bounded machine recognizing L. DTIME(T (n)) = fL(M) : M is det. and T (n) time boundedg NTIME(T (n)) = fL(M) : M is T (n) time boundedg DSPACE(S(n)) = fL(M) : M is det. and S(n) space boundedg NSPACE(S(n)) = fL(M) : M is S(n) space boundedg . – p.2/17 Hierarchy theorems A function S(n) is space constructible iff there is S(n)-bounded machine that given w writes aS(jwj) on the tape and stops. A function T (n) is time constructible iff there is a machine that on a word of size n makes exactly T (n) steps. Thm: Let S2(n) be a space-constructible function and let S1(n) ≥ log(n). If S1(n) lim infn!1 = 0 S2(n) then DSPACE(S2(n)) − DSPACE(S1(n)) is nonempty. Thm: Let T2(n) be a time-constructible function and let T1(n) log(T1(n)) lim infn!1 = 0 T2(n) then DTIME(T2(n)) − DTIME(T1(n)) is nonempty.