Problems

• Checking Identity
  • $\text{EQ}(a,b)=[a=b]$ (bit-string $x\in{0,1}^n$)
  • Communication cost
• checking distinctness
  • Input: $A,B\in{1,2,\cdots,n}$
  • Determine whether $A=B$(multiset equivalence)
• counting distinct elements
  • Data: Stream Model, $n$ is unknown and elements is presented one at a time
  • Input: a sequence $x_1,x_2,\cdots,x_n\in\Omega$
  • Output: an estimation of $z=|{x_1,x_2,\cdots,x_n}|$
• Set Membership
  • Data: a set of elements $x_1,\cdots,x_n\in\Omega$
  • Input: $x\in\Omega$
  • Output: $[x\in\Omega]$
• Frequency Estimation
  • Data: a sequence of elements $x_1,\cdots,x_n\in\Omega$
  • Input: $x\in\Omega$
  • Output: $f_x=|{i|x_i=x}$|

Fingerprint

• use Fingerprint to design One-sided-error Monte Carlo algorithm
  • $\text{FING}(\cdot)$: function easy to compute and compare
  • $X=Y,\text{FING}(X)=\text{FING}(Y)$
  • $X\not=Y,P(\text{FING}(X)=\text{FING}(Y))$ is small

Communication Protocol

• Theorem (Yao 1979): Any deterministic communication protocol computing EQ on two $n$-bit strings costs $n$ bits of communication in the worst-case.
• Fingerprinting by PIT
  • $\text{FING}(x)=\sum_{i=0}^{n-1}x^ir^i$ under $\mathbb{Z}_p$, $r$ chosen uniformly at random
  • $x\neq y,P(\text{FING}(x)=\text{FING}(y))\leq\frac{n-1}{p}$
    • $p\in[n^2,2n^2],P\leq\frac{1}{n}$
  • communication cost: $O(\log p)$
• Fingerprinting by check sum
  • $\text{FING}(x)=x\bmod p,p\in[k],k=2n^2\ln n$
  • $x\neq y,P(\text{FING}(x)=\text{FING}(y))=P(|x-y|\bmod p=0)\leq\frac{n}{\pi(k)}\leq\frac{1}{n}$
  • Proof technique
    • number of prime divisors of $n\leq \log_2n$
    • number of primes in $[k]$: $\pi(k)\sim\frac{k}{\ln k}$
  • communication cost: $O(\log k)=O(\log n)$

Checking distinctness

• $\text{FING}(A)=f_A(r)=\prod_{i=1}^n(r-a_i),r\in \mathbb{Z}_p$
  • $p\in [(n\log n)^2,2(n\log n)^2]$
  • Theorem (Lipton 1989): $A\not=B,P(\text{FING}(A)=\text{FING}(b))=O(\frac{1}{n})$
  • Proof technique
    • $A\not=B,P(\text{FING}(A)=\text{FING}(B))=P(f_A(r)=f_B(r)|f_A\not\equiv f_B)P(f_A\not\equiv f_B)+P(f_A(r)=f_B(r)|f_A\equiv f_B)P(f_A\equiv f_B)$
  • space cost: $O(\log p)=O(\log n)$

Hashing

Distinct Elements

• deterministic: $O(n)$ space
• $(\epsilon,\delta)$-estimator $\hat Z$: $P((1-\epsilon)z\leq \hat Z\leq(1+\epsilon)z))\geq1-\delta$
  • $\epsilon$: approximation error
  • $\delta$: confidence error
• UHA(uniform hash assumption)
  • $h:[N]\rightarrow[M]$ takes $\Omega(N\log M)$ bits according to information theory
  • SUHA(Simple UHA): A uniform random function $h:[N]\rightarrow[M]$ is available and the computation of $h$ is efficient.
• Hashing Estimator
  • $h:\Omega\rightarrow[0,1]$
  • Compute $h(x_i)$: independent values in $[0,1]$
  • $Z=\frac{1}{\min_ih(x_i)}-1$
    • $E(\min_{1\leq i\leq n}h(x_i))=E($length of a subinterval$)=\frac{1}{z+1}$
    • drawback: $V(\min_ih(x_i))$ is too large
• Flajolet-Martin algorithm (1985)
  • $\overline{Y}=\frac{1}{k}\sum_{j=1}^k Y_j$
  • $E(\overline{Y})=\frac{1}{z+1}$
  • $Z=\frac{1}{\overline{Y}}-1$
  • $k\geq\lceil\frac{4}{\epsilon^2\delta}\rceil,\forall \epsilon,\delta<\frac{1}{2}$
  • Space cost: $O(\epsilon^{-2}\log n)$ bits
• 2010: $\Theta(\epsilon^{-2}+\log n)$

Sketch

• sketch: lossy representation of data and tolerate a bounded error in answering queries

Set Membership

• Dictionary data structure
  • balanced tree: $O(n\log|\Omega|)$ space, $O(\log n)$ time
  • perfect hashing: $O(n\log|\Omega|)$ space, $O(1)$ time
• Bloom filter (1970)
  • $h_1,h_2,\cdots,h_k:\Omega\rightarrow [cn]$ are uniform and independent random hash function
  • Data Structure $A$ of $cn$ bits $O(n)$
    • initially: all $0$
    • for each $x\in S,A[h_i(x)]=1$ for $1\leq i\leq k$
  • Query: yes if $A[h_i(x)]=1,1\leq i\leq k$
    • time cost: $O(k)$
  • $\text{RP}$
    • $x\in S$: always correct
    • $x\not\in S$:
      • $P(A[h_1(x)]=0)=(1-\frac{1}{cn})^{kn}\approx e^{\frac{-k}{c}}$
      • $P=(1-e^{\frac{-k}{c}})^k$
      • $k=c\ln 2,P=(0.6185)^c$

Frequency Estimation

• Additive error: $P(|\hat f_x-f_x|\leq\epsilon n)\geq 1-\delta$
  • heavy hitters: elements $x$ with $f_x>\epsilon n$
• Count-min sketch (Cormode and Muthukrishnan, 2003)
  • Data Structure: $k\times m$ integer array $\text{CMS}[k][m]$
    • initially: $\text{CMS}[k][m] = 0$
    • for each $x_i$, $\text{CMS}[j][h_j(x_i)]$++
  • Query: $\hat f_x=\min_{1\leq j\leq k}\text{CMS}[j][h_j(x)]$
    • Time cost: $O(\frac{1}{\epsilon})$
  • Space cost: $O(km\log n)=O(\frac{1}{\epsilon}\log\frac{1}{\delta}\log n)$
  • Proof
    • $\text{CMS}[j][h_j(x)]\geq f_x$
    • $\mathbb{E}(\text{CMS}[j][h_j(x)])\leq f_x+\frac{n}{m}$
    • $P(|\hat f_x-f_x|\leq\epsilon n)\leq\frac{1}{\epsilon m}^k$
    • $m=\lceil\frac{e}{\epsilon}\rceil,k=\lceil\ln\frac{1}{\delta}\rceil$