10,197 research outputs found
Trustee: Full Privacy Preserving Vickrey Auction on top of Ethereum
The wide deployment of tokens for digital assets on top of Ethereum implies
the need for powerful trading platforms. Vickrey auctions have been known to
determine the real market price of items as bidders are motivated to submit
their own monetary valuations without leaking their information to the
competitors. Recent constructions have utilized various cryptographic protocols
such as ZKP and MPC, however, these approaches either are partially
privacy-preserving or require complex computations with several rounds. In this
paper, we overcome these limits by presenting Trustee as a Vickrey auction on
Ethereum which fully preserves bids' privacy at relatively much lower fees.
Trustee consists of three components: a front-end smart contract deployed on
Ethereum, an Intel SGX enclave, and a relay to redirect messages between them.
Initially, the enclave generates an Ethereum account and ECDH key-pair.
Subsequently, the relay publishes the account's address and ECDH public key on
the smart contract. As a prerequisite, bidders are encouraged to verify the
authenticity and security of Trustee by using the SGX remote attestation
service. To participate in the auction, bidders utilize the ECDH public key to
encrypt their bids and submit them to the smart contract. Once the bidding
interval is closed, the relay retrieves the encrypted bids and feeds them to
the enclave that autonomously generates a signed transaction indicating the
auction winner. Finally, the relay submits the transaction to the smart
contract which verifies the transaction's authenticity and the parameters'
consistency before accepting the claimed auction winner. As part of our
contributions, we have made a prototype for Trustee available on Github for the
community to review and inspect it. Additionally, we analyze the security
features of Trustee and report on the transactions' gas cost incurred on
Trustee smart contract.Comment: Presented at Financial Cryptography and Data Security 2019, 3rd
Workshop on Trusted Smart Contract
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization
Data-driven algorithm design, that is, choosing the best algorithm for a
specific application, is a crucial problem in modern data science.
Practitioners often optimize over a parameterized algorithm family, tuning
parameters based on problems from their domain. These procedures have
historically come with no guarantees, though a recent line of work studies
algorithm selection from a theoretical perspective. We advance the foundations
of this field in several directions: we analyze online algorithm selection,
where problems arrive one-by-one and the goal is to minimize regret, and
private algorithm selection, where the goal is to find good parameters over a
set of problems without revealing sensitive information contained therein. We
study important algorithm families, including SDP-rounding schemes for problems
formulated as integer quadratic programs, and greedy techniques for canonical
subset selection problems. In these cases, the algorithm's performance is a
volatile and piecewise Lipschitz function of its parameters, since tweaking the
parameters can completely change the algorithm's behavior. We give a sufficient
and general condition, dispersion, defining a family of piecewise Lipschitz
functions that can be optimized online and privately, which includes the
functions measuring the performance of the algorithms we study. Intuitively, a
set of piecewise Lipschitz functions is dispersed if no small region contains
many of the functions' discontinuities. We present general techniques for
online and private optimization of the sum of dispersed piecewise Lipschitz
functions. We improve over the best-known regret bounds for a variety of
problems, prove regret bounds for problems not previously studied, and give
matching lower bounds. We also give matching upper and lower bounds on the
utility loss due to privacy. Moreover, we uncover dispersion in auction design
and pricing problems
Computer-aided verification in mechanism design
In mechanism design, the gold standard solution concepts are dominant
strategy incentive compatibility and Bayesian incentive compatibility. These
solution concepts relieve the (possibly unsophisticated) bidders from the need
to engage in complicated strategizing. While incentive properties are simple to
state, their proofs are specific to the mechanism and can be quite complex.
This raises two concerns. From a practical perspective, checking a complex
proof can be a tedious process, often requiring experts knowledgeable in
mechanism design. Furthermore, from a modeling perspective, if unsophisticated
agents are unconvinced of incentive properties, they may strategize in
unpredictable ways.
To address both concerns, we explore techniques from computer-aided
verification to construct formal proofs of incentive properties. Because formal
proofs can be automatically checked, agents do not need to manually check the
properties, or even understand the proof. To demonstrate, we present the
verification of a sophisticated mechanism: the generic reduction from Bayesian
incentive compatible mechanism design to algorithm design given by Hartline,
Kleinberg, and Malekian. This mechanism presents new challenges for formal
verification, including essential use of randomness from both the execution of
the mechanism and from the prior type distributions. As an immediate
consequence, our work also formalizes Bayesian incentive compatibility for the
entire family of mechanisms derived via this reduction. Finally, as an
intermediate step in our formalization, we provide the first formal
verification of incentive compatibility for the celebrated
Vickrey-Clarke-Groves mechanism
Information in Mechanism Design
We survey the recent literature on the role of information for mechanism design. We specifically consider the role of endogeneity of and robustness to private information in mechanism design. We view information acquisition of and robustness to private information as two distinct but related aspects of information management important in many design settings. We review the existing literature and point out directions for additional future work.Mechanism Design, Information Acquisition, Ex Post Equilibrium, Robust Mechanism Design, Interdependent Values, Information Management
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