Civitas Verifiability and Coercion Resistance for Remote Voting Virginia Tech NCR September 14, 2012 Michael Clarkson George Washington University with.

Slides:



Advertisements
Similar presentations
Receipt-Free Universally-Verifiable Voting With Everlasting Privacy Tal Moran.
Advertisements

Secret Ballot Receipts: True Voter Verifiable Elections Author: David Chaum Published: IEEE Security & Privacy Presenter: Adam Anthony.
Pretty Good Democracy James Heather, University of Surrey
RPC Mixing: Making Mix-Nets Robust for Electronic Voting Ron Rivest MIT Markus Jakobsson Ari Juels RSA Laboratories.
Vote privacy: models and cryptographic underpinnings Bogdan Warinschi University of Bristol 1.
Talk by Vanessa Teague, University of Melbourne Joint work with Chris Culnane, James Heather & Steve Schneider at University of.
Requirements for a Secure Voting System  Only authorized voters can vote  No one can vote more than once  No one can determine for whom anyone else.
The Italian Academic Community’s Electronic Voting System Pierluigi Bonetti Lisbon, May 2000.
Civitas Verifiability and Coercion Resistance for Remote Voting University of South Alabama August 15, 2012 Michael Clarkson The George Washington University.
Civitas Security and Transparency for Remote Voting Swiss E-Voting Workshop September 6, 2010 Michael Clarkson Cornell University with Stephen Chong (Harvard)
ThreeBallot, VAV, and Twin Ronald L. Rivest – MIT CSAIL Warren D. Smith - CRV Talk at EVT’07 (Boston) August 6, 2007 Ballot Box Ballot Mixer Receipt G.
ETen E-Poll ID – Strasbourg COE meeting November, 2006 Slide 1 E-TEN E-POLL Project Electronic Polling System for Remote Operation Strasbourg.
James Heather, University of Surrey Peter Y A Ryan, University of Luxembourg Vanessa Teague, University of Melbourne.
Cryptographic Voting Protocols: A Systems Perspective Chris Karlof Naveen Sastry David Wagner UC-Berkeley Direct Recording Electronic voting machines (DREs)
1 Receipt-freedom in voting Pieter van Ede. 2 Important properties of voting  Authority: only authorized persons can vote  One vote  Secrecy: nobody.
1 Introduction CSE 5351: Introduction to cryptography Reading assignment: Chapter 1 of Katz & Lindell.
Trustworthy Services from Untrustworthy Components: Overview Fred B. Schneider Department of Computer Science Cornell University Ithaca, New York
Receipt-Free Universally-Verifiable Voting With Everlasting Privacy Tal Moran Joint work with Moni Naor.
Civitas Toward a Secure Voting System Michael Clarkson Cornell University Coin (ca. 63 B.C.) commemorating introduction of secret ballot in 137 B.C. Stevens.
Civitas A Secure Remote Voting System Michael Clarkson, Stephen Chong, Andrew Myers Cornell University Dagstuhl Seminar on Frontiers of Electronic Voting.
Electronic Voting Presented by Ben Riva Based on presentations and papers of: Schoenmakers, Benaloh, Fiat, Adida, Reynolds, Ryan and Chaum.
Vanessa Teague Department of Computer Science and Software Engineering University of Melbourne Australia.
Self-Enforcing E-Voting (SEEV) Feng Hao Newcastle University, UK CryptoForma’13, Egham.
Receipt-free Voting Joint work with Markus Jakobsson, C. Andy Neff Ari Juels RSA Laboratories.
Research & development A Practical and Coercion-resistant scheme for Internet Voting Jacques Traoré (joint work with Roberto Araújo and Sébastien Foulle)
Client/Server Computing Model of computing in which very powerful personal computers (clients) are connected in a network with one or more server computers.
© VoteHere, Inc. All rights reserved. November 2004 VHTi Data Demonstration Andrew Berg Director, Engineering.
CMSC 414 Computer and Network Security Lecture 6 Jonathan Katz.
CMSC 414 Computer (and Network) Security Lecture 2 Jonathan Katz.
Oblivious Transfer based on the McEliece Assumptions
10/25/20061 Threshold Paillier Encryption Web Service A Master’s Project Proposal by Brett Wilson.
1 Enforcing Confidentiality in Low-level Programs Andrew Myers Cornell University.
Receipt-freeness and coercion-resistance: formal definitions and fault attacks Stéphanie Delaune / Steve Kremer / Mark D. Ryan.
CMSC 414 Computer and Network Security Lecture 9 Jonathan Katz.
CMSC 414 Computer and Network Security Lecture 6 Jonathan Katz.
Static Validation of a Voting ProtocolSlide 1 Static Validation of a Voting Protocol Christoffer Rosenkilde Nielsen with Esben Heltoft Andersen and Hanne.
Alexander Potapov.  Authentication definition  Protocol architectures  Cryptographic properties  Freshness  Types of attack on protocols  Two-way.
Civitas Toward a Secure Voting System AFRL Information Management Workshop October 22, 2010 Michael Clarkson Cornell University.
Cryptographic Voting Protocols: A Systems Perspective By Chris Karlof, Naveen Sastry, and David Wagner University of California, Berkely Proceedings of.
Computer Security Tran, Van Hoai Department of Systems & Networking Faculty of Computer Science & Engineering HCMC University of Technology.
KYUSHUUNIVERSITYKYUSHUUNIVERSITY SAKURAILABORATORYSAKURAILABORATORY Sakurai Lab. Kyushu University Dr-course HER, Yong-Sork E-voting VS. E-auction.
Masked Ballot Voting for Receipt-Free Online Elections Sam Heinith, David Humphrey, and Maggie Watkins.
Containment and Integrity for Mobile Code Security policies as types Andrew Myers Fred Schneider Department of Computer Science Cornell University.
6. Esoteric Protocols secure elections and multi-party computation Kim Hyoung-Shick.
Andreas Steffen, , LinuxTag2009.ppt 1 LinuxTag 2009 Berlin Verifiable E-Voting with Open Source Prof. Dr. Andreas Steffen Hochschule für Technik.
Online voting: a legal perspective
Research & development Towards Practical Coercion-Resistant Electronic Elections Jacques Traoré France Télécom / Orange Labs SecVote 2010 Bertinoro - Italy.
Coercion-Resistant Remote Voting Michael Clarkson Cornell University Coin (ca. 63 B.C.) commemorating introduction of secret ballot in 137 B.C. SecVote.
SECURE WEB APPLICATIONS VIA AUTOMATIC PARTITIONING S. Chong, J. Liu, A. C. Myers, X. Qi, K. Vikram, L. Zheng, X. Zheng Cornell University.
A remote voting system based on Prêt à Voter coded by David Lundin Johannes Clos.
Lecture 16: Security CDK4: Chapter 7 CDK5: Chapter 11 TvS: Chapter 9.
TGDC Meeting, July 2010 Security Considerations for Remote Electronic UOCAVA Voting Andrew Regenscheid National Institute of Standards and Technology
Privacy and Anonymity Using Mix Networks* Slides borrowed from Philippe Golle, Markus Jacobson.
Electronic Voting R. Newman. Topics Defining anonymity Need for anonymity Defining privacy Threats to anonymity and privacy Mechanisms to provide anonymity.
Secure Remote Electronic Voting CSE-681 Fall 2006 David Foster and Laura Stapleton Laura StapletonLaura Stapleton.
Introduction to Network Systems Security Mort Anvari.
CSCE 715: Network Systems Security Chin-Tser Huang University of South Carolina.
Secure, verifiable online voting 29 th June 2016.
Recipt-free Voting Through Distributed Blinding
ThreeBallot, VAV, and Twin
Motivation Civitas RCF Security Properties of E-Voting protocols
Civitas Michael Clarkson Cornell Stephen Chong Harvard
Basic Network Encryption
CDK4: Chapter 7 CDK5: Chapter 11 TvS: Chapter 9
CDK: Chapter 7 TvS: Chapter 9
The Italian Academic Community’s Electronic Voting System
Basic Network Encryption
Ronald L. Rivest MIT ShafiFest January 13, 2019
Presentation transcript:

Civitas Verifiability and Coercion Resistance for Remote Voting Virginia Tech NCR September 14, 2012 Michael Clarkson George Washington University with Stephen Chong (Harvard) and Andrew Myers (Cornell)

2 INTEGRITYCONFIDENTIALITY

Remote 3 (including Internet) INTEGRITYCONFIDENTIALITY

Mutual Distrust 4 KEY PRINCIPLE:

INTEGRITY 5 Universal verifiability Voter verifiability Eligibility verifiability UV: [Sako and Killian 1994, 1995] EV & VV: [Kremer, Ryan & Smyth 2010]

Why Verifiability? People: –Corrupted programmers –Hackers (individuals, …, nation-states) Software: –Buggy code –Malware Trustworthiness: fair elections are a basis of representative democracy 6

CONFIDENTIALITY 7 Coercion resistance better than receipt freeness or simple anonymity RF: [Benaloh 1994] CR: [Juels, Catalano & Jakobsson 2005]

Why Coercion Resistance? Protect election from improper influence Protect people from fear of reprisal Realize ideals of voting booth, remotely Trustworthiness: fair elections are a basis of representative democracy 8

AVAILABILITY 9 Tally availability

Security Properties Original system: Universal verifiability Eligibility verifiability Coercion resistance Follow-up projects: Voter verifiability Tally availability 10 …under various assumptions

11 JCJ Voting Scheme [Juels, Catalano & Jakobsson 2005] Proved universal verifiability and coercion resistance Civitas extends JCJ

12 Civitas Architecture bulletin board voter client tabulation teller registration teller ballot box

13 Registration voter client registration teller Voter retrieves credential share from each registration teller; combines to form credential

Credentials Verifiable Unsalable Unforgeable Anonymous 14

15 Voting voter client ballot box Voter submits copy of encrypted choice and credential to each ballot box

Resisting Coercion: Fake Credentials 16

17 Resisting Coercion If the coercer demands that the voter… Then the voter… Submits a particular voteDoes so with a fake credential. Sells or surrenders a credential Supplies a fake credential. AbstainsSupplies a fake credential to the adversary and votes with a real one.

18 Tabulation bulletin board tabulation teller ballot box Tellers retrieve votes from ballot boxes

19 Tabulation bulletin board tabulation teller Tabulation tellers anonymize votes; eliminate unauthorized (and fake) credentials; decrypt remaining choices.

20 Auditing bulletin board Anyone can verify proofs that tabulation is correct ballot box

21 Civitas Architecture bulletin board voter client tabulation teller registration teller ballot box Universal verifiability: Tellers post proofs during tabulation Coercion resistance: Voters can undetectably fake credentials S ECURITY P ROOFS

22 Protocols –El Gamal; distributed [Brandt]; non-malleable [Schnorr and Jakobsson] –Proof of knowledge of discrete log [Schnorr] –Proof of equality of discrete logarithms [Chaum & Pederson] –Authentication and key establishment [Needham-Schroeder- Lowe] –Designated-verifier reencryption proof [Hirt & Sako] –1-out-of-L reencryption proof [Hirt & Sako] –Signature of knowledge of discrete logarithms [Camenisch & Stadler] –Reencryption mix network with randomized partial checking [Jakobsson, Juels & Rivest] –Plaintext equivalence test [Jakobsson & Juels] Implementation: 21k LoC

Trust Assumptions 23

24 Trust Assumptions 1. “Cryptography works.” 2. The adversary cannot masquerade as a voter during registration. 3. Voters trust their voting client. 4. At least one of each type of authority is honest. 5. The channels from the voter to the ballot boxes are anonymous. 6. Each voter has an untappable channel to a trusted registration teller.

25 Trust Assumptions 1. “Cryptography works.” 2. The adversary cannot masquerade as a voter during registration. 3. Voters trust their voting client. 4. At least one of each type of authority is honest. 5. The channels from the voter to the ballot boxes are anonymous. 6. Each voter has an untappable channel to a trusted registration teller. Universal verifiability Coercion resistance Coercion resistance

26 Trust Assumptions 1. “Cryptography works.” 2. The adversary cannot masquerade as a voter during registration. 3. Voters trust their voting client. 4. At least one of each type of authority is honest. 5. The channels from the voter to the ballot boxes are anonymous. 6. Each voter has an untappable channel to a trusted registration teller.

27 Trust Assumptions 1. “Cryptography works.” 2. The adversary cannot masquerade as a voter during registration. 3. Voters trust their voting client. 4. At least one of each type of authority is honest. 5. The channels from the voter to the ballot boxes are anonymous. 6. Each voter has an untappable channel to a trusted registration teller.

Registration 28 In person. In advance. Con:System not fully remote Pro:Credential can be used in many elections

29 Trust Assumptions 1. “Cryptography works.” 2. The adversary cannot masquerade as a voter during registration. 3. Voters trust their voting client. 4. At least one of each type of authority is honest. 5. The channels from the voter to the ballot boxes are anonymous. 6. Each voter has an untappable channel to a trusted registration teller.

Eliminating Trust in Voter Client 30 VV: Use challenges (like Helios, VoteBox) CR: Open problem

31 Trust Assumptions 1. “Cryptography works.” 2. The adversary cannot masquerade as a voter during registration. 3. Voters trust their voting client. 4. At least one of each type of authority is honest. 5. The channels from the voter to the ballot boxes are anonymous. 6. Each voter has an untappable channel to a trusted registration teller.

32 Trust Assumptions` 1. “Cryptography works.” 2. The adversary cannot masquerade as a voter during registration. 3. Voters trust their voting client. 4. At least one of each type of authority is honest. 5. The channels from the voter to the ballot boxes are anonymous. 6. Each voter has an untappable channel to a trusted registration teller.

33 Trust Assumptions 1. “Cryptography works.” 2. The adversary cannot masquerade as a voter during registration. 3. Voters trust their voting client. 4. At least one of each type of authority is honest. 5. The channels from the voter to the ballot boxes are anonymous. 6. Each voter has an untappable channel to a trusted registration teller.

Untappable Channel 34 Minimal known assumption for receipt freeness and coercion resistance Eliminate? Open problem. (Eliminate trusted registration teller? Also open.)

35 Trust Assumptions 1. “Cryptography works.” 2. The adversary cannot masquerade as a voter during registration. 3. Voters trust their voting client. 4. At least one of each type of authority is honest. 5. The channels from the voter to the ballot boxes are anonymous. 6. Each voter has an untappable channel to a trusted registration teller.

Trusted procedures? 36

Time to Tally 37

38 Tabulation Time # voters in precinct = K, # tab. tellers = 4, security strength ≥ 112 bits [NIST 2011–2030]

39 Summary Can achieve strong security and transparency: –Remote voting –Universal (voter, eligibility) verifiability –Coercion resistance Security is not free: –Stronger registration (untappable channel) –Cryptography (computationally expensive)

Assurance 40 Security proofs (JCJ, us)Secure implementation (Jif)

Ranked Voting 41

42 Open Problems Coercion-resistant voter client? Voter-verifiable voter client? Eliminate untappable channel in registration? Credential management? Usability? Application-level denial of service?

43 Technical Issues Web interfaces BFT bulletin board Threshold cryptography Anonymous channel integration

as (google “civitas voting”)

Civitas Verifiability and Coercion Resistance for Remote Voting Virginia Tech NCR September 14, 2012 Michael Clarkson George Washington University with Stephen Chong (Harvard) and Andrew Myers (Cornell)

46 Extra Slides

47 Adversary Always: –May perform any polynomial time computation –May corrupt all but one of each type of election authority  Distributed trust Almost always: –May control network –May coerce voters, demanding secrets or behavior, remotely or physically Security properties: Confidentiality, integrity, availability

48 Paper What paper does: –Convince voter that his vote was captured correctly What paper does next: –Gets dropped in a ballot box –Immediately becomes insecure Chain-of-custody, stuffing, loss, recount attacks… Hacking paper elections has a long and (in)glorious tradition [Steal this Vote, Andrew Gumbel, 2005] 20% of paper trails are missing or illegible [Michael Shamos, 2008] What paper doesn’t: –Guarantee that a vote will be counted –Guarantee that a vote will be counted correctly

49 Cryptography “The public won’t trust cryptography.” –It already does… –Because experts already do “I don’t trust cryptography.” –You don’t trust the proofs, or –You reject the hardness assumptions

50 Selling Votes Requires selling credential… –Which requires: Adversary tapped the untappable channel, or Adversary authenticated in place of voter… –Which then requires: Voter transferred ability to authenticate to adversary; something voter… –Has: too easy –Knows: need incentive not to transfer –Is: hardest to transfer

51 Civitas Policy Examples Confidentiality: –Information: Voter’s credential share –Policy: “RT permits only this voter to learn this information” –Jif syntax: RT  Voter Confidentiality: –Information: Teller’s private key –Policy: “TT permits no one else to learn this information” –Jif syntax: TT  TT Integrity: –Information: Random nonces used by tellers –Policy: “TT permits only itself to influence this information” –Jif syntax: TT  TT

52 Civitas Policy Examples Declassification: –Information: Bits that are committed to then revealed –Policy: “TT permits no one to read this information until all commitments become available, then TT declassifies it to allow everyone to read.” –Jif syntax: TT  [TT  commAvail  ] Erasure: –Information: Voter’s credential shares –Policy: “Voter requires, after all shares are received and full credential is constructed, that shares must be erased.” –Jif syntax: Voter  [Voter credConst  T ]

53 Blocks Block is a “virtual precinct” –Each voter assigned to one block –Each block tallied independently of other blocks, even in parallel Tabulation time is: –Quadratic in block size –Linear in number of voters If using one set of machines for many blocks –Or, constant in number of voters If using one set of machines per block

54 Tabulation Time K = 100 sequential parallel

55 Ranked Voting Voters submit ranking of candidates –e.g., Condorcet, Borda, STV –Help avoid spoiler effects –Defend against strategic voting –“Italian attack” Civitas implements coercion-resistant Condorcet, approval and plurality voting methods –Could do any summable method

56 Secure Implementation In Jif [Myers 1999, Chong and Myers 2005, 2008] –Security-typed language –Types contain information-flow policies Confidentiality, integrity, declassification, erasure If policies in code express correct requirements… –(And Jif compiler is correct…) –Then code is secure w.r.t. requirements

57 CPU Cost For reasonable security parameters, CPU time is 39 sec / voter / authority. If CPUs are bought, used (for 5 hours), then thrown away: $1500 / machine ) $12 / voter If CPUs are rented: $1 / CPU / hr ) 4¢ / voter Increased cost…Increased security