weizmann institute of science israel

Weizmann Institute of ScienceIsrael

Deterministic History-IndependentStrategies for Storing Information

on Write-Once Memories

Tal Moran Moni Naor Gil Segev

Weizmann Institute of ScienceIsrael

Securing Vote Storage Mechanisms

Tal Moran Moni Naor Gil Segev

3

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 …

Alice

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Election Day

Carol

AliceBob 11

1

1

Carol Alice Alice Bob What about more involved election systems?

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

A simple solution: Lexicographically sorted list of

candidates Unary counters

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Secure Vote Storage Mechanisms that operate in extremely hostile environments

Without a “secure” mechanism an adversary may be able to Undetectably tamper with the records Compromise privacy

Possible scenarios: Poll workers may tamper with the device while in transit Malicious software embeds secret information in public output …

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Main Security Goals Tamper-evidence

Prevent an adversary from undetectably tampering with the records

History-independenceMemory representation does not reveal the insertion order

Subliminal-freenessInformation cannot be secretly embedded into the data

Integrity

Privacy

This Work

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Goal:A secure and efficient mechanism for storing an increasingly

growing set of K elements taken from a large universe of size N

Why consider a large universe? Write-in candidates Votes which are subsets or rankings Records may contain additional information (e.g., 160-bit hash values)

Supports Insert(x), Seal() and RetreiveAll()Cast a ballot

Count votes

“Finalize” the elections

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This WorkGoal:

A secure and efficient mechanism for storing an increasingly growing set of K elements taken from a large universe of size N

Tamper-evidence by exploiting write-once memories Due to Molnar, Kohno, Sastry & Wagner ’06 Information-theoretic security Everything is public!! No need for private storage

Deterministic strategy in which each subset of elements determines a unique memory representation

Strongest form of history-independence Unique representation - cannot secretly embed information

Our approach:

Initialized to all 0’sCan only flip 0’s to 1’s

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Previous approaches were either: Inefficient (required O(K2) space) Randomized (enabled subliminal channels) Required private storage

Explicit

Space

Insertion time

Kpolylog(N)polylog(N)

Klog(N/K)log(N/K)

Non-Constructive

Deterministic, history-independent and write-oncestrategy for storing an increasingly growing set of K

elements taken from a large universe of size N

Our ResultsMain

Result

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Deterministic, history-independent and write-oncestrategy for storing an increasingly growing set of K

elements taken from a large universe of size N

Our ResultsMain

Result

First explicit, deterministic and non-adaptive Conflict Resolution algorithm which is optimal

up to poly-logarithmic factors

Application to Distributed Computing

Resolve conflicts in multiple-access channels One of the classical Distributed Computing problems Explicit, deterministic & non-adaptive -- open since ‘85 [Komlos &

Greenberg]

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Previous Work Molnar, Kohno, Sastry & Wagner ‘06

Initiated the formal study of secure vote storage Tamper-evidence by exploiting write-once memories

Initialized to all 0’sCan only flip 0’s to 1’s

Encoding(x) = (x, wt2(x))

Logarithmic overhead

PROM

Flipping any bit of x from 0 to 1requires flipping a bit of wt2(x)

from 1 to 0

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Previous Work Molnar, Kohno, Sastry & Wagner ‘06

Initiated the formal study of secure vote storage Tamper-evidence by exploiting write-once memories “Copy-over list”: A deterministic & history-independent solution

Problem: Cannot sort in-place on write-once

memories

On every insertion: Compute the sorted list including the new element Copy the sorted list to the next available memory position Erase the previous list

A useful observation [Naor & Teague ‘01]:Store the elements in a lexicographically sorted list

O(K2) space!!

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Previous Work Molnar, Kohno, Sastry & Wagner ‘06

Initiated the formal study of secure vote storage Tamper-evidence by exploiting write-once memories “Copy-over list”: A deterministic & history-independent solution Several other solutions which are either randomized or require private storage

Bethencourt, Boneh & Waters ‘07 A linear-space cryptographic solution “History-independent append-only” signature scheme Randomized & requires private storage

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Our Mechanism Global strategy

Mapping elements to entries of a table

Both strategies are deterministic, history-independent and write-once

Local strategy Resolving collisions separately in each entry

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The Local Strategy Store elements mapped to each entry in a separate copy-over list

ℓ elements require ℓ2 pre-allocated memory Allows very small values of ℓ in the worst case!

Can a deterministic global strategy guarantee that?

The worst case behavior of any fixed hash function is very poor There is always a relatively large set of elements which are mapped

to the same entry….

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The Global Strategy Sequence of tables Each table stores a fraction of the elements

Each element is inserted into several entries of the first table When an entry overflows:

o Elements that are not stored elsewhere are inserted into the next tableo The entry is permanently deleted

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The Global Strategy Each element is inserted into several entries of the first table When an entry overflows:

o Elements that are not stored elsewhere are inserted into the next tableo The entry is permanently deleted

Universe of size N

OVERFLOW

OVERFLOW

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The Global Strategy

OVERFLOW

Universe of size N

Each element is inserted into several entries of the first table When an entry overflows:

o Elements that are not stored elsewhere are inserted into the next tableo The entry is permanently deleted

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Each element is inserted into several entries of the first table When an entry overflows:

o Elements that are not stored elsewhere are inserted into the next tableo The entry is permanently deleted

Universe of size N

Unique representation: Elements determine

overflowing entries in the first table

Elements mapped to non-overflowing entries are stored

Continue with the next table and remaining elements

The Global Strategy

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Subset of size K

Table of size ~KStores ®K elements

Table of size ~(1-®)KStores ®(1 - ®)K

elements

Table of size ~(1-®)2K

Where do the hash functions come from?

Universe of size N

Each element is inserted into several entries of the first table When an entry overflows:

o Elements that are not stored elsewhere are inserted into the next tableo The entry is permanently deleted

The Global Strategy

Identify the hash function of each table with a bipartite graph

Universe of size N

S

OVERFLOW

OVERFLOW

LOW DEGREE

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The Global Strategy

(K, ®, ℓ)-Bounded-Neighbor Expander:Any set S of size K contains ®K element with a neighbor of degree · ℓ w.r.t S

Bounded-Neighbor Expanders

Table of size M

Universe of size N

Given N and K, want to optimize M, ℓ, ® and the left-degree D

Optimal Extractor Disperser

1 polylog(N)

1/2

M

®

ℓ

1/2

K¢log(N/K)

K¢2(loglogN)2 K

1/polylog(

N)

O(1)

(K, ®, ℓ)-Bounded-Neighbor Expander:Any set S of size K contains ®K element with a neighbor of degree · ℓ w.r.t S

log(N/K)D 2(loglogN)2 polylog(N)

Open Problems Non-amortized insertion time

In our scheme insertions may have a cascading effect Construct a scheme that has bounded worst case insertion time

Improved bounded-neighbor expanders

The monotone encoding problem Our non-constructive solution: Klog(N) log(N/K) bits Obvious lower bound: Klog(N/K) bits Find the minimal M such that subsets of size at most K taken

from [N] can be mapped into subsets of [M] while preserving inclusions

Alon & Hod ‘07: M = O(Klog(N/K))23

Conflict Resolution Problem: resolve conflicts that arise when several parties transmit

simultaneously over a single channel Goal: schedules retransmissions such that each of the conflicting parties

eventually transmits individually A party which successfully transmits halts Efficiency measure: number of steps it takes to resolve any K conflicts

among N parties An algorithm is non-adaptive if the choices of the parties in each step do

not depend on previous steps

Conflict Resolution Why require a deterministic algorithm?

Radio Frequency Identification (RFID) Many tags simultaneously read by a single reader

Inventory systems, product tracking,... Tags are highly constraint devices

Can they generate randomness?

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The Algorithm Global strategy

Mapping parties to time intervals

Local strategy Resolving collisions separately in each interval

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The Local Strategy Associate each party x 2 [N] with a codeword C(x) taken from a

superimposed code:Any codeword is not contained in the bit-wise or of any other ℓ-1 codewords

Resolves conflicts among any ℓ parties taken from [N]

Party x transmits at step i if and only if C(x)i = 1

O(ℓ2¢logN) steps using known explicit constructions

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Sequence of phases identified with bounded-neighbor expanders Each phase contains several time slots The graphs define the active parties at each slot Resolve collisions in each slot using the local strategy

Universe of size N

The Global Strategy

Phase 1

Phase 2

Phase 3

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Sequence of phases identified with bounded-neighbor expanders Each phase contains several time slots The graphs define the active parties at each slot Resolve collisions in each slot using the local strategy

Universe of size N

The Global Strategy

O(K¢polylog(N))

steps

OVERFLOW

OVERFLOW

SUCCESS

SUCCESSSUCCESS