On worst-case to average-case reductions for NP
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On worst-case to average-case reductions for NP
Danny Gutfreund (Harvard) Ronen Shaltiel (Haifa U.) and
Amnon Ta-Shma (Tel-Aviv U.)
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Negative results
Thm: [BT,FF]
If PH does not collapse, then there is no non-adaptive
reduction
from solving SAT to solving (L,U) for some L in NP.
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Reduction = Turing reduction
A computational task A reduces to computational task B, if
There exists an efficient oracle machine R,
such that for any O, if O solves B then RO solves A.
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The [BT] results generalizes
Thm: If PH does not collapse* then there is no non-adaptive reduction
from solving SAT to solving (SAT,D).
Where D is any distribution samplable in quasi-polynomial time.
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Search to decision reduction
RB is the search-algorithm for SAT, using B as a decision algorithm for
SAT.
Note: Can be a non-adaptive reduction.
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GST
Thm: [GST] There exists some distribution D
samplable in quasi-polynomial time, such that
If BSAT is a probabilistic, polynomial time algorithm solving (SAT,D) well on the average,
Then, RBSAT solves SAT.
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In other words
There is a reduction from solving SAT to solving
(SAT,D), where D is some distribution
samplable in quasi-polynomial time.
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Reductions again
When we say “reduction” we mean several things:
We mean that R has black-box access to O (that solves B).
We mean that RO is correct whenever O is.
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More on reductions The first condition tells us that the
reduction does not need to know about the actual way B operates.
The second condition tells us that the correctness proof does not need to know about the way B operates.
These are two separate issues!!!
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Class Reduction
A computational task A C-reduces to computational task B, if
there exists an efficient oracle machine R,
such that for any O in C, if O solves B then RO solves A.
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We saw
If PH does not collapse*
- One can not achieve the GST reduction with a non-adaptive reduction, but
- One can achieve the GST reduction with a non-adaptive, BPP-class reduction.
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So
Now, that we have no negative results to stop us,
Can we make progress on the worst-case to avg-case problem for NP?
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The cryptographic goal
Prove a polynomial-time reduction from SAT to (L,D), for some
L in NP, and polynomially samplable D.
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IL If (L,D) is average-case hard for
some L in NP and samplable D, then
(L,U) is average-case hard for some L in NP.
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A more modest goal
Prove a polynomial-time class reduction from SAT to (L,U), for L in NTime(t(n)).
t(n) =nc – cryptographic setting t(n) =super-poly(n) – complexity
setting
NOT KNOWN for any sub-exponential t(n)
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Have vs. Want: Have: A polynomial-time class
reduction from SAT to (SAT,D), for D samplable in super-polynomial time.
Want: A polynomial-time class reduction from SAT to (L,U), for L in .
PN~
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Idea: use [IL]
(SAT,D )not in AvgBPP, D is super-poly
(L,U )not in AvgBPP, L is super-poly
SAT not in BPP
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ProblemThe reduction time depends on the
complexity of D.
Not useful.
We get an algorithm for (L,D) taking more resources than D, which [GST] does not contradict.
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The main theorem Under a weak derandomizaion
assumption:
Thm: There exists L in s.t.,
BPPAvgULBPPNP o )1(2/1),(
PN~
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The Assumption in detail
For every c, for every probabilistic polynomial-time
A using nc coins, There exists a probabilistic polynomial
time algorithm A’, using only n coins, s.t.
For any samplable distribution DPr {x in D} [ |A(x) – A’(x)| ≥ 1/10 ] ≤ 1/10
L
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The proof In spite of all, let use IL.
Observation: the complexity of the reduction can be made to depend on the number of coins of D, and not on the running time of D.
The new language depends on the running time of D.
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The main idea While we do not know how to save
on time, we believe we can save on random coins.
Use the derandomization assumption to reduce the number of coins of D.
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The reality It works but takes effort. We need to derandomize
procedures that output non-boolean values, which we usually can not derandomize.
This forces us to go back to [GST] and modify the proof to get the derandomized version.
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Summary Negative results showed there are no
non-adaptive worst-case to average-case reduction.
We show class reductions exist, where regular reductions are ruled out.
Can we now solve the complexity version of the worst-case to average-case reduction for NP?