brown university - cra · -- edward snowden @ sxsw‘14. encrypted search soluons 13. usage 14 tk...

Encrypted Search

Seny Kamara

Brown University

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Q: Why is this happening?

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Big Data ►  Industry and Governments want more data ►  NaDonal security ►  Machine learning ►  Business analyDcs ►  NLP ►  LocaDon-based services ►  …

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Big Data

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u  More intrusive & sensitive u  Photos, medical records

u  Location data, email,

u  browsing history, voicemails

u  Greater need for security

u  Harder to secure u  NSA Bluffdale holds 2EBs! (2K PBs)

u  Facebook holds 300PBs of photos/videos

u  Vs. nation states, intelligence agencies, organized crime, insiders, …

Big Data

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u  Impossible to work with u  Lose search, DBs, IR

u  Find your photo among 300PBs?

u  Rank results?

u  End-to-end (e2e) encryption! u  Reduces attack surface

u  Secure small key instead of Big Data

Q: Can we search on encrypted data?

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An InteresDng QuesDon

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Cryptography

Data Structures

InformaDon Retrieval

Graph Theory

Databases

Combinatorial OpDmizaDon

StaDsDcs

A LucraDve QuesDon

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►  Startups ►  CipherCloud ($30M+$50M) ►  Navajo (Salesforce) ►  SkyHigh , Vaultive, Inpher ►  Bitglass, Private Machines, …

►  Major Corporations ►  Microsoft, IBM, ►  Google, Yahoo ►  Hitachi, Fujitsu

►  Funding agencies ►  IARPA ►  DARPA ►  NSF

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“There are a lot of advancements in things like encrypted search...but in general it is a difficult problem”

-- Edward Snowden @ SXSW‘14

Encrypted Search SoluDons

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Usage

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tk

EDB

DB

Desiderata

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tk

EDB

Storage leakage

Query leakage

Size of EDB

Search Dme

Size of tk

Many Approaches ►  Stream ciphers [SWP01]

►  BuckeDng [HILM02]

►  Structured and searchable encrypDon (StE/SSE) [SWP01,CGKO06,CK10]

►  Oblivious RAM (ORAM) [GO96]

►  FuncDonal encrypDon (e.g., PEKS) [BCOP06]

►  MulD-party computaDon (MPC) [Yao82,GMW87]

►  Property-preserving encrypDon (PPE) [AKSX04,BBO06,BCLO09]

►  Fully-homomorphic encrypDon [G09]

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Tradeoffs: Efficiency vs. Security

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Efficiency STE/SSE-based

PPE-based

FHE-based

ORAM-based

skFE-based pkFE-based

Leakage

Tradeoffs: FuncDonality vs. Efficiency

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SK-FE-based STE/SSE-based

PPE-based FHE-based

ORAM-based

PK-FE-based

Efficiency

FuncDonality

SQL

NoSQL

Leakage

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►  TheoreDcal Cryptography [Goldwasser-Micali82,…] ►  A great success story ►  Helps us reason about confidenDality, integrity, … ►  Focused on leakage-free cryptography

►  Real-world systems security relies on tradeoffs ►  No cryptographic foundaDons for tradeoffs ►  Can we leak X but not Y? ►  How do we model leakage?

Leakage [Curtmola-Garay-K.-Ostrovsky06, Chase-K.10, Islam-Kuzu-Kantarcioglu12, K.15]

►  Leakage analysis: what is being leaked?

►  Proof: prove that soluDon leaks no more

►  Cryptanalysis: can we exploit the leakage?

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Leakage analysis Proof of security Leakage cryptanalysis

ApplicaDons

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Encrypted Search Engines ►  Desktop search ►  Windows search, Apple Spotlight

►  Personal cloud storage ►  Dropbox, OneDrive, iCloud, …

►  Webmail ►  Gmail, Yahoo! Mail, Outlook.com,…

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Encrypted DBs ►  Standard DBs ►  DB encrypted in memory

►  Cloud DBs ►  DB encrypted in cloud

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Encrypted NSA Metadata Program [K.14]

►  To & from numbers, Dme of call, duraDon for all US-to-US, US-to-Foreign and Foreign-to-US calls

►  NSA DB can only be queried by individual phone number (seed)

►  Analyst queries must be approved by small number of NSA officials

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Systems (Provably Secure)

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Systems ►  CS2 (C++) ►  Microsos Research, 2012 ►  Queries: single keyword search ►  16MB email collecDon in 53ms

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►  BlindSeer (C++) [IARPA] ►  Columbia & Bell Labs, 2014 ►  Queries: boolean ►  SyntheDc dataset ►  Search Dme ►  For (w1 and w2): 250ms ►  w1 in 1 docs ►  w2 in 10K docs

Systems

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►  IBM-UCI (C++) [IARPA] ►  IBM Research & UC Irvine, 2013 ►  Queries: conjuncDve ►  1.3GB email collecDon ►  Search Dme ►  For (w1 and w2): 5ms ►  w1 in 15 docs ►  w2 in 1M docs

►  Clusion (Java) ►  Brown & Colorado St., 2016 ►  Queries: Boolean ►  1.3GB email collecDon ►  Search Dme ►  For (w1 or w2) and (w3 or w4) in 1.5ms

►  (w1 or w2) in 10 docs ►  (w3 or w4) in 1M docs

Systems ►  GRECS ►  Microsos Research, Boston U., Harvard & Ben Gurion, 2015 ►  Queries: (approximate) shortest distance on graphs ►  1.6M nodes & 11M edges ►  Query Dme: 10ms

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Conclusions ►  ExciDng and acDve area of research

►  Big potenDal impact in pracDce

►  Lots of new research direcDons in theory and systems

►  PotenDal for collaboraDon between many areas of CS ►  Algorithms and data structures ►  Databases ►  InformaDon retrieval ►  Combinatorial opDmizaDon ►  StaDsDcs

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