430 research outputs found
PS-TRUST: Provably Secure Solution for Truthful Double Spectrum Auctions
Truthful spectrum auctions have been extensively studied in recent years.
Truthfulness makes bidders bid their true valuations, simplifying greatly the
analysis of auctions. However, revealing one's true valuation causes severe
privacy disclosure to the auctioneer and other bidders. To make things worse,
previous work on secure spectrum auctions does not provide adequate security.
In this paper, based on TRUST, we propose PS-TRUST, a provably secure solution
for truthful double spectrum auctions. Besides maintaining the properties of
truthfulness and special spectrum reuse of TRUST, PS-TRUST achieves provable
security against semi-honest adversaries in the sense of cryptography.
Specifically, PS-TRUST reveals nothing about the bids to anyone in the auction,
except the auction result. To the best of our knowledge, PS-TRUST is the first
provably secure solution for spectrum auctions. Furthermore, experimental
results show that the computation and communication overhead of PS-TRUST is
modest, and its practical applications are feasible.Comment: 9 pages, 4 figures, submitted to Infocom 201
Enabling Privacy-preserving Auctions in Big Data
We study how to enable auctions in the big data context to solve many
upcoming data-based decision problems in the near future. We consider the
characteristics of the big data including, but not limited to, velocity,
volume, variety, and veracity, and we believe any auction mechanism design in
the future should take the following factors into consideration: 1) generality
(variety); 2) efficiency and scalability (velocity and volume); 3) truthfulness
and verifiability (veracity). In this paper, we propose a privacy-preserving
construction for auction mechanism design in the big data, which prevents
adversaries from learning unnecessary information except those implied in the
valid output of the auction. More specifically, we considered one of the most
general form of the auction (to deal with the variety), and greatly improved
the the efficiency and scalability by approximating the NP-hard problems and
avoiding the design based on garbled circuits (to deal with velocity and
volume), and finally prevented stakeholders from lying to each other for their
own benefit (to deal with the veracity). We achieve these by introducing a
novel privacy-preserving winner determination algorithm and a novel payment
mechanism. Additionally, we further employ a blind signature scheme as a
building block to let bidders verify the authenticity of their payment reported
by the auctioneer. The comparison with peer work shows that we improve the
asymptotic performance of peer works' overhead from the exponential growth to a
linear growth and from linear growth to a logarithmic growth, which greatly
improves the scalability
Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges
In recent years, blockchain has gained widespread attention as an emerging
technology for decentralization, transparency, and immutability in advancing
online activities over public networks. As an essential market process,
auctions have been well studied and applied in many business fields due to
their efficiency and contributions to fair trade. Complementary features
between blockchain and auction models trigger a great potential for research
and innovation. On the one hand, the decentralized nature of blockchain can
provide a trustworthy, secure, and cost-effective mechanism to manage the
auction process; on the other hand, auction models can be utilized to design
incentive and consensus protocols in blockchain architectures. These
opportunities have attracted enormous research and innovation activities in
both academia and industry; however, there is a lack of an in-depth review of
existing solutions and achievements. In this paper, we conduct a comprehensive
state-of-the-art survey of these two research topics. We review the existing
solutions for integrating blockchain and auction models, with some
application-oriented taxonomies generated. Additionally, we highlight some open
research challenges and future directions towards integrated blockchain-auction
models
Negotiable Auction Based on Mixed Graph: A Novel Spectrum Sharing Framework
© 2015 IEEE. Auction-based spectrum sharing is a promising solution to improve the spectrum utilization in 5G networks. Along with the spatial reuse, we observe that the ability to adjust the coverage of a spectrum bidder can provide room to itself for further negotiation while auctioning. In this paper, we propose a novel economic tool, size-negotiable auction mechanism (SNAM), which provides a hybrid solution between auction and negotiation for multi-buyers sharing spectrum chunks from a common database. Unlike existing auction-based spectrum sharing models, each bidder of the SNAM submits its bid for using the spectrum per unit space and a set of coverage ranges over which the bidder is willing to pay for the spectrum. The auctioneer then coordinates the interference areas (or coverage negotiation) to ensure no two winners interfere with each other while aiming to maximize the auction's total coverage area or revenue. In this scenario, the undirected graph used by existing auction mechanisms fails to model the interference among bidders. Instead, we construct a mixed interference graph and prove that SNAM's auctioning on the mixed graph is truthful and individually rational. Simulation results show that, compared with existing auction approaches, the proposed SNAM dramatically improves the spatial efficiency, hence leads to significantly higher seller revenue and buyer satisfaction under various setups. Thanks to its low complexity and low overhead, SNAM can target fine timescale trading (in minutes or hours) with a large number of bidders and requested coverages
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