Skip to main content
USENIX
  • Conferences
  • Students
Sign in
  • Home
  • Attend
    • Venue, Hotel, and Travel
    • Students and Grants
    • Co-Located Workshops
  • Program
    • At a Glance
    • Technical Sessions
    • Poster Session
  • Activities
    • Birds-of-a-Feather Sessions
    • Poster Session
    • WiPs
  • Participate
    • Call for Papers
      • Important Dates
      • Symposium Organizers
      • Symposium Topics
      • Refereed Papers
      • Shadow PC
      • Symposium Activities
      • Submitting Papers
    • Instructions for Participants
  • Sponsorship
  • About
    • Symposium Organizers
    • Services
    • Questions
    • Help Promote!
    • Past Symposia
  • Home
  • Attend
    • Venue, Hotel, and Travel
    • Students and Grants
    • Co-Located Workshops
  • Program
  • Activities
  • Participate
    • Call for Papers
    • Instructions for Participants
  • Sponsorship
  • About
    • Symposium Organizers
    • Services
    • Questions
    • Help Promote!
    • Past Symposia

sponsors

Platinum Sponsor
Gold Sponsor
Gold Sponsor
Silver Sponsor
Silver Sponsor
Silver Sponsor
Bronze Sponsor
Bronze Sponsor
General Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Media Sponsor
Industry Partner
Industry Partner

help promote

USENIX Security '16 button

Get more
Help Promote graphics!

connect with usenix


  •  Twitter
  •  Facebook
  •  LinkedIn
  •  Google+
  •  YouTube

twitter

Tweets by USENIXSecurity

usenix conference policies

  • Event Code of Conduct
  • Conference Network Policy
  • Statement on Environmental Responsibility Policy

You are here

Home » M2R: Enabling Stronger Privacy in MapReduce Computation
Tweet

connect with us

M2R: Enabling Stronger Privacy in MapReduce Computation

Authors: 

Tien Tuan Anh Dinh, Prateek Saxena, Ee-Chien Chang, Beng Chin Ooi, and Chunwang Zhang, National University of Singapore

Abstract: 

New big-data analysis platforms can enable distributed computation on encrypted data by utilizing trusted computing primitives available in commodity server hardware. We study techniques for ensuring privacy preserving computation in the popular MapReduce framework. In this paper, we first show that protecting only individual units of distributed computation (e.g. map and reduce units), as proposed in recent works, leaves several important channels of information leakage exposed to the adversary. Next, we analyze a variety of design choices in achieving a stronger notion of private execution that is the analogue of using a distributed oblivious-RAM (ORAM) across the platform. We develop a simple solution which avoids using the expensive ORAM construction, and incurs only an additive logarithmic factor of overhead to the latency. We implement our solution in a system called M2R, which enhances an existing Hadoop implementation, and evaluate it on seven standard MapReduce benchmarks. We show that it is easy to port most existing applications to M2R by changing fewer than 43 lines of code. M2R adds fewer than 500 lines of code to the TCB, which is less than 0:16% of the Hadoop codebase. M2R offers a factor of 1:3x to 44:6x lower overhead than extensions of previous solutions with equivalent privacy. M2R adds a total of 17% to 130% overhead over the insecure baseline solution that ignores the leakage channels M2R addresses.

Tien Tuan Anh Dinh, National University of Singapore

Prateek Saxena, National University of Singapore

Ee-Chien Chang, National University of Singapore

Beng Chin Ooi, National University of Singapore

Chunwang Zhang, National University of Singapore

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

Dinh PDF
View the slides

Presentation Video 

Presentation Audio

MP3 Download

Download Audio

  • Log in or    Register to post comments

Open access to the USENIX Security '15 videos sponsored by Symantec.

Platinum Sponsors

Gold Sponsors

Silver Sponsors

Bronze Sponsors

General Sponsors

Media Sponsors & Industry Partners

Open Access Publishing Partner

© USENIX

  • Privacy Policy
  • Contact Us