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Ronen ShaltielSergei Artemenko University of Haifa

2 g g Function g:{0,1} n →{0,1} is p-hard for a family of circuits if for every circuit in this family Pr x←U n [C(x)=g(x)]

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Circuits fail to compute some inputs Circuits fail to compute noticeable fraction of inputs Almost random guessing Hard on worst caseMildly average-case hardStrongly average-case hard p=1 p=1-δp= ½+ε For simplicity assume δ=¹⁄₁₀

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Derandomization, Pseudorandomness [Yao82, BM84, NW94,…] Cryptographic primitives [Yao82, BM84,…] These applications require functions that are very hard on average p=½+negligible

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gf strongly average-case hard g=Amp(f) f worst case hard f or f mildly average-case hard f Example: Yao’s XOR lemma (δ=¹⁄₁₀) f gff If function f(x) is (1-¹⁄₁₀)-hard for circuits of size at most s, then function g(x 1,…,x k )=f(x 1 )⊕⋯⊕f(x k ) is (½+ε)-hard for circuits of size at most s'=s·poly(ε)~~
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gf strongly average-case hard g=Amp(f) f worst case hard f or f mildly average-case hard f f Assumption: f is worst case/mildly average-case hard for circuits of size at most s. Example: Direct product/concatenation lemma (δ=¹⁄₁₀) f gff If a function f(x) is (1-¹⁄₁₀)-hard for circuits of size at most s, then function g(x 1,…,x k )=f(x 1 )∘⋯∘f(x k ) is ε-hard for circuits of size at most s'=s·poly(ε)~~
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gf In all hardness amplification results in literature target function g=Amp(f) is hard for circuits of size s'~~
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Circuits of size at most s Circuits of size at most s' Natural question: Is this size loss necessary? We will show that size loss is necessary for certain proof techniques.

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f f is (1-δ)–hard for size s g g is (½+ε)-hard for size s' g ∃D of size s' such that Pr[D(y)=g(y)] ≥ ½+ε f ∃C of size s such that Pr[C(x)=f(x)]≥1-δ Proof by reduction: Existence of circuit C is shown by providing a reduction R (an oracle procedure) s.t. C=R D. iff

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“Uniform”: R (·) is an “efficient” oracle TM. Known: These types of reductions cannot prove most hardness amplification results in literature [STV99]. f “Non-uniform”: R (·) is a “small” oracle circuit that is also allowed to receive a “short advice string” α as a function of f and more importantly of the oracle D supplied to R. “Semi-uniform”: R (·) is a “small” oracle circuit. More precisely: A non-uniform reduction R (·) satisfies: ∀D s.t. Pr[D(y)=g(y)]≥½+ε ff ∃α=α(f,D) s.t. Pr[R D (x,α)=f(x)]≥1-δ Essentially all known hardness amplification results are proven using such reductions

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In this work we show that every reduction must make q=Ω (¹⁄ ε ) queries. s'≤ε·s size loss! If reduction R makes ≤ q queries to oracle D, then circuit C can be constructed by replacing every oracle gate with circuit D. s=size(C)≈q·size(D)+size(R)≥q·size(D)=q·s'

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Theorem*: Every reduction R (·) must make q=Ω (¹⁄ ε ) queries to oracle even if R (·) is non-uniform and adaptive (i.e., it makes adaptive queries). *For standard parameters of hardness amplification. Comparison to [SV10]: [SV10] only handle non-uniform non-adaptive reductions. g Our results apply to a more general class of hardness amplification tasks (non-Boolean g, errorless amplification, “function-specific amplification”). [SV10] gives a better bound of q=Ω ( log(¹⁄ δ ) ⁄ ε 2 ) for Boolean case. (Our results apply to a more general setup in which there are upper bounds of q=Ω ( log(¹⁄ δ ) ⁄ ε ).

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fg Given functions f,g consider (distribution over) oracles D : With probability 2ε, D(y)=g(y). With probability 1-2ε, D(y) answers a fresh random bit. ⇒ Pr[D(y)=g(y)]≥½+ε (so that R D has to approx. compute f). Folklore e.g. [R]: A reduction R (·) that makes o(¹⁄ ε ) queries is unlikely to get any meaningful information. f R D cannot compute f (even approximately). Contradiction (meaning that # of queries = Ω(¹⁄ ε ) ). Difficulties for general reductions: Non-uniform reductions can use advice string to locate queries y on which D answers correctly. Furthermore, adaptability may allow a non-uniform reduction to find “interesting” queries y (based on the adaptive strategy of whether or not previous queries answer).

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Difficulties for general reductions: Non-uniform reductions can use advice string to locate queries y on which D answers correctly. Furthermore, adaptability may allow a non-uniform reduction to find “interesting” queries y (based whether or not previous queries answer). Our approach: Following [SV10] we show that advice string does not help a non-adaptive reduction to find queries that answer (except for few queries which we can handle). For adaptive reductions, consider “hybrid executions” of R D : ◦ First t queries are not answered. ◦ Remaining q-t queries are answered according to oracle distribution. Hybrid executions are in some sense non-adaptive (the t+1’st query is known in advance). We first bound the information that R gets on g in hybrid executions. Then we show that with high probability real and hybrid executions coincide.

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Size loss is inherent in reductions showing hardness amplification even in the most general case (non-uniform and adaptive reductions). Not an impossibility result for hardness amplification: only rules out certain proof techniques. Limitations apply to essentially all proof techniques in literature. See discussion in paper. Our lower bounds on # of queries match upper bounds in some (but not all) settings: ◦ Direct product lemma with constant δ [KS03]. ◦ Errorless amplification with constant δ [BS07,W11]. Open: Improve lower bounds to match upper bounds: ◦ For non-constant δ. ◦ For Boolean target function. Can we develop other proof techniques for hardness amplification? (See e.g., [GST05,A06,GT07]).

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