History-IndependentCuckoo Hashing

Weizmann InstituteIsrael

Udi WiederMoni Naor Gil Segev

Microsoft Research Silicon Valley

2

Election DayCarol

Bob

Carol

Elections for class president Each student whispers in Mr. Drew’s ear Mr. Drew writes down the votes

Alice Alice Bob

Alice Problem:

Mr. Drew’s notebook leaks sensitive information First student voted for Carol Second student voted for Alice …

AliceMay compromise the

privacy of the elections

3

Election Day

Carol

AliceBob

11

1

1

Carol Alice Alice Bob What about more involved applications?

Write-in candidates Votes which are subsets or rankings ….

A simple solution: Lexicographically sorted list of

candidates Unary counters

4

Learning From History

A simple example: sorted list Canonical memory representation Not really efficient...

The two levels of a data structure “Legitimate” interface Memory representation

History independence The memory representation should not reveal information that cannot be obtained using the legitimate interface

AliceBob

Carol

5

Typical Applications

Incremental cryptography [BGG94, Mic97]

Voting [MKSW06, MNS07]

Set comparison & reconciliation [MNS08]

Computational Geometry [BGV08]

...

6

Our ContributionThe first HI dictionary that simultaneously achieves the following:

Efficiency: Lookup time – O(1) worst case Update time – O(1) expected amortized Memory utilization 50% (25% with deletions)

Strongest notion of history independence

Simple and fast

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Notions of History Independence Micciancio (1997): oblivious trees

Motivated by incremental cryptography Only considered the shape of the trees and not their memory

representation

Naor and Teague (2001) Memory representation Weak & strong history independence

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Notions of History Independence Weak history independence

Memory revealed at the end of an activity period Any two sequences of operations S1 and S2 that lead to the same

content induce the same distribution on the memory representation

Strong history independence Memory revealed several times during an activity period Any two sets of breakpoints along S1 and S2 with the same content

at each breakpoint, induce the same distributions on the memory representation at all these points

Completely randomizing memory after each operation is not good enough.

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Notions of History Independence

Weak & strong are not equivalent WHI for reversible data structures is possible without a canonical

representation Provable efficiency gaps [BP06] (in restricted models)

We consider strong history independence Canonical representation (up to initial randomness) implies SHI Other direction shown to hold for reversible data structures

[HHMPR05]

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SHI DictionariesDeletions

Memory utilization

Update time

Lookup time

Practical?

Naor & Teague ‘01

Blelloch & Golovin ‘07

Blelloch & Golovin ‘07

This work

99%

99%

< 9%

> 25%(> 50%)

O(1) expected

O(1) expected

O(1) expected

O(1) expected

O(1) worst case

O(1) expected

O(1) worst case

O(1) worst case

?

(mem. util. < 50%)

(mem. util. < 50%)

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Our Approach Cuckoo hashing [PR01]:

A simple & practical scheme with worst case constant lookup time

Force a canonical representation on cuckoo hashing No significant loss in efficiency

Avoid rehashing by using a small stash What happens when hash functions fail? Rehashing is highly problematic in SHI data structures

All hash functions need to be sampled in advance When an item is deleted, may need to roll back on previous functions

We use a secondary storage to reduces the failure probability exponentially [KMW08]

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Cuckoo Hashing Tables T1 and T2 with hash functions h1 and h2

Store x in one of T1[h1(x)] and T2[h2(x)]

Insert(x): Greedily insert in T1 orT2

if both are full insert in T1

Repeat in other table with the previous occupant (if any)

Y

Z

V

T1 T2

X

Z

Y

V

T1 T2

X

Successful insertion

W W

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Cuckoo Hashing Tables T1 and T2 with hash functions h1 and h2

Store x in one of T1[h1(x)] and T2[h2(x)]

Y

U

Z

V

T1 T2

X

Failure –rehash

required

Insert(x): Greedily insert in T1 orT2

if both are full insert in T1

Repeat in other table with the previous occupant (if any)

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The Cuckoo Graph Set S ½ U containing n keys h1, h2 : U ! {1,...,r}

Bipartite graph with sets of size rEdge (h1(x), h2(x)) for every x2S

S is successfully stored

Every connected componenthas at most one cycle

Main theorem:

If r ¸ (1 + ²)n and h1,h2 are log(n)-wise independent,then failure probability is £(1/n)

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The Canonical Representation Assume that S can be stored using h1 and h2 We force a canonical representation on the cuckoo graph

Suffices to consider a single connected component

Assume that S forms a tree in the cuckoo graph. Typical case

One location must be empty. The choice of the empty location uniquely determines the location of all elements

a

b

d

c

eRule: h1 (minimal element) is empty

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The Canonical Representation Assume that S can be stored using h1 and h2 We force a canonical representation on the cuckoo graph

Suffices to consider a single connected component

Assume that S has one cycle Two ways to assign elements in the

cycle Each choice uniquely determines the

location of all elements

a

b

d

c

eRule: minimal element in cycle lies in T1

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The Canonical Representation Updates efficiently maintain the canonical representation Insertions:

New leaf: check if new element is smaller than current min new cycle:

Same component… Merging two components…

All cases straight forward

Update time < size of component = expected (small) constant

Deletions: Find the new min, split component,… Requires connecting all elements in the component with a cyclic list

Memory utilization drops to 25% All cases straight forward

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Rehashing What if S cannot be stored using h1 and h2 ?

Happens with probability 1/n Can we simply pick new functions?

Canonical memory implies we need to sample all hash functions in advance

Whenever an item is deleted, need to check whether we can role back to previous hash functions

A bad item which is repeatedly inserted and deleted would cause a rehash every operation!

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Using a Stash Whenever an insert fails, put a ‘bad’ item in a secondary data

structure Bad item: smallest item that belongs to a cycle Secondary data structure must be SHI in itself

Theorem [KMW08]: Pr[|stash| > s] < n-s

In practice keeping the stash as a sorted list is probably the best solution Effectively the query time is constant with (very) high probability

In theory the stash could be any SHI with constant lookup time A deterministic hashing scheme, where the elements are rehashed

whenever the content changes [AN96, HMP01]

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Conclusions and Problems Cuckoo hashing is a robust and flexible hashing scheme

Easily ‘molded’ into a history independent data structure

We don’t know how to analyze variants with more than 2 hash functions and/or more than 1 element per bucket Expected size of connected component is not constant

Full performance analysis