Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Implementing Privacy Preserving Auction Protocols (Master Thesis Final Talk)

Markus Teich, B. Sc. [email protected]

March 23, 2017

Chair of Network Architectures and Services Department of Informatics Technical University of Munich Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Motivation

In many existing auction systems sellers can: • Abuse loosing bid information • Collude with bidder(s) and change the outcome

Our goals are: • Reduce requirement of trust in auction platform operators • Create usably fast system

M. Teich — Auctions 2 Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Related Work

Cryptographic auction protocols provide: • Correctness of the outcome • Non-Repudiation • Various degrees of privacy We choose the work of Felix Brandt because: • No trusted third party required • Beneficial privacy properties • Reviewed

M. Teich — Auctions 3 first price first price private outcome public outcome M + 1st price M + 1st price private outcome public outcome

Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Auction Formats

Sealed bid vs. Incrementing

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Auction Formats

Sealed bid vs. Incrementing

first price first price private outcome public outcome M + 1st price M + 1st price private outcome public outcome

M. Teich — Auctions 4 Chair of Network Architectures and Services Department of Informatics Technical University of Munich libbrandt Overview

• git://.org/libbrandt • Auction Outcome determination (all four formats) • Algorithms by Felix Brandt translated to Ed25519 • Around 4000 lines of • Depends on libgcrypt v1.7+ and libgnunetutil v13.0+ • GPLv3+

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Research Questions

• How does the architecture look like? • How much do we improve performance by using Ed25519? • How scalable is the system?

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Architecture

Figure 1: Simplified System Architecture

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GNUnet Auction

ADF := auction description file • create: Initiate seller, output: ADF • join: Join auction as bidder, input: ADF • info: Retrieve status of running auctions, extract info from ADF

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Research Questions

X How does the architecture look like? • How much do we improve performance by using Ed25519? • How scalable is the system?

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Measurement Setup

• Lenovo X240 notebook computer • Core i7-4600U CPU (2.1GHz) • Single CPU core used • Generated 3072 bit RSA key with ssh-keygen • Bids chosen uniformly at random • Median of ∼ 10 iterations

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Measurement Results 1

Figure 2: First Price Private Outcome Auction with five Prices.

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Measurement Results 2

Figure 3: First Price Private Outcome Auction with five Bidders.

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Wassenberg Heuristic

Figure 4: Possible (t, u) Pairs for M = 3, n = 7 and any k ≥ n.

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Measurement Results 3

Figure 5: M + 1st Price Private Outcome Auction with n = k = M + 2.

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Measurement Results 4

Figure 6: All libbrandt Algorithms with 512 Prices.

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Performance Conclusion

Compared to RSA:

• Computation cost reduced to 7% • Bandwidth cost reduced to 8% • Wassenberg Heuristic worth looking into

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Research Questions

X How does the architecture look like? X How much do we improve performance by using Ed25519? • How scalable is the system?

M. Teich — Auctions 17 Given a good multicast-implementation the systems scalability only de- pends on the network speed and computing power of the seller and the bidders.

Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Scalability

• Computations in O(n3) • Participating in several auctions on the same host is problematic. • Only Sellers and bidders involved → No need to scale a platform or auctioneer

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Scalability

• Computations in O(n3) • Participating in several auctions on the same host is problematic. • Only Sellers and bidders involved → No need to scale a platform or auctioneer

Given a good multicast-implementation the systems scalability only de- pends on the network speed and computing power of the seller and the bidders.

M. Teich — Auctions 18 Depending on the auction format we need between 4kiB/s and 20kiB/s. → computation cost > bandwidth cost.

Moore’s Law is faster than Nielsen’s Law.

Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Computation Cost vs. Bandwidth Cost

Which network speed leads to equal time consumption?

M. Teich — Auctions 19 → computation cost > bandwidth cost.

Moore’s Law is faster than Nielsen’s Law.

Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Computation Cost vs. Bandwidth Cost

Which network speed leads to equal time consumption?

Depending on the auction format we need between 4kiB/s and 20kiB/s.

M. Teich — Auctions 19 Moore’s Law is faster than Nielsen’s Law.

Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Computation Cost vs. Bandwidth Cost

Which network speed leads to equal time consumption?

Depending on the auction format we need between 4kiB/s and 20kiB/s. → computation cost > bandwidth cost.

M. Teich — Auctions 19 Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Computation Cost vs. Bandwidth Cost

Which network speed leads to equal time consumption?

Depending on the auction format we need between 4kiB/s and 20kiB/s. → computation cost > bandwidth cost.

Moore’s Law is faster than Nielsen’s Law.

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Research Questions

X How does the architecture look like? X How much do we improve performance by using Ed25519? X How scalable is the system?

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Final Status

• https://gnunet.org/libbrand-thesis • More measurement data • Detailed algorithm and protocol description • Speed suffices for real world usage • Architecture design

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Future Work

• GNUnet Auction system (GSoC 2017) • Escrowed payment service using GNU Taler • Auction distribution platform • Price pool distribution • Reputation system for sellers

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Closing

Q&A

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