resilient functions - video.ias.edu · eshan chattopadhyay ias area of research: theoretical...
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Resilient Functions
Eshan Chattopadhyay IAS
Area of Research: Theoretical Computer Science, Combinatorics
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Collective Coin-Flipping
Player 1 Player n
f: {0,1}n→{0,1}
b: unbiased Bit
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An Adversarial Model
f
b
Malicious Coalition of players Q ⊂ [n]:
• Adaptively sends bits AFTER seeing coin flips of other players.
• PARITY FAILS!
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Majority works better…
X: # of heads in (n-q) random coin flips
Pr[X ∈[n/2 - q,n/2+q]]= O(q/√n)
q malicious players
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Influence of Sets
• Influence of Q: Probability output of f can bechanged by Q after the ‘good players’ flip their coins
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More formally…f: {0,1}n→{0,1}
Pr[ f(X) is NOT constant] = Influence of Q on f
X:
Bits in Q: unfixedBits sampled uniformly
Q ⊂ [n]
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Resilient Functions
(q,ε)-resilient function: ∀ Q ⊂ [n], |Q| =q, Influence of Q on f is at most ε.
f: {0,1}n→{0,1}
Example: MAJORITY is (n0.49,ε)-resilient.
PARITY is NOT (1, ε)-resilient, any ε <1.
Assume 𝐄[f]=1/2.
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t(n,ε)= max{q :∃ a (q,ε)-resilient function f: {0,1}n→{0,1}, 𝐄[f]=1/2}
• Key to understanding limits of coin-flipping games
• Basic Question about Boolean functions
Limits on resilience
Rest of the talk: Upper and Lower Bounds on t(n,ε)
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Upper Bound on t(n,ε)• Kahn-Kalai-Linial ’88: ∃ a coordinate with influence
(log n)/n.
• Edge Isoperimetry→ ∃ coordinate with influence 1/n
• Induction gives O(n/log n) coordinates with influence Ω(1).
t(n,0.1) ≤ n / log n
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Lower Bound on t(n,ε)• t(n,0.1) = Ω(√n) Majority
• t(n,0.1) = Ω(n0.63) Recursive Majority [Ben-Or Linial 88]
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Lower Bound on t(n,ε)
• Ajtai-Linial 1990: There exists a (n/log2n)-resilient function that is almost balanced.
• Probabilistic construction
t(n,0.1) ≥ n / log2 n
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Explicit resilient functions
• Recall: Resilient functions imply coin flipping protocols.
Reference ResilienceMajority √n
Recursive Majority [BenOr-Linial 85] n0.63
[Meka, C-Zuckerman 16] n0.99
[Meka 16] n/log2n
[C-Zuckerman 16], [Meka 16] : Based on derandomizing Ajtai-Linial
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A bit more about the construction in [C-Zuckerman 16]
C is monotone and can be computed fast in parallel.
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An Application: Explicit Ramsey graphs [C-Zuckerman 16]
N N
KKX
Y
Bipartite K-Ramsey graph: Bipartite graph with NO complete or empty K×K sub-graph.
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Explicit Ramsey graphs
Ramsey (1928): Does not exist (log N)/2-Ramsey graphs
Erdos (1947): ∃ 2log N-Ramsey graphs
Erdos: Explicit Constructions?
N N
KKX
Y
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Explicit Ramsey Graphs (N=2n, K=2k)
Reference K Bipartite
Erdös 47 (existential) ≥ 2 log N Yes
Hadamard Matrix √N Yes
Frankl-Wilson81, Naor92, Alon98, Grolmusz00, Ba
Gopalan062Ω(√(log N log log N)) No
Pudlak-Rödl 04 √N/2√log N Yes
Barak-Kindler-Shaltiel-Sudakov-Wigderson 10 Nδ Yes
Barak-Rao-Shaltiel -Wigderson 12 (log N)2√log log N Yes
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Explicit Ramsey graphs
Corollary of [C-Zuckerman 16]: Explicit (log N)poly(log log N)-Ramsey graph
• Independent work [Cohen 16] achieves similar parameters.
N N
KKX
Y
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General Coin-Flipping Games
• Internal Nodes: Labeled by players
• Leaves: Labeled by 0 or 1 (output of the protocol)
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General Coin-Flipping Games
• Well studied Model [BN 85, Saks 89, AN 90, BopN93, RZ98, RSZ99,F99]
• Protocols can handle (1/2- ℇ)n sized adversaries.
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Open Directions
• Close the gap: n / log2 n ≤ t(n,0.1) ≤ n / log n
• Resilience of functions on larger domains. • f: [0,1]n→{0,1}
• Known: n/log2 n ≤ t(n,0.1) < n / 2
• More applications.
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Thanks!
Questions?