174 research outputs found
PADS: Privacy-preserving Auction Design forAllocating Dynamically Priced Cloud Resources
With the rapid growth of Cloud Computing technologies, enterprises are increasingly deploying their services in the Cloud. Dynamically priced cloud resources such as the Amazon EC2 Spot Instance provides an efficient mechanism for cloud service providers to trade resources with potential buyers using an auction mechanism. With the dynamically priced cloud resource markets, cloud consumers can buy resources at a significantly lower cost than statically priced cloud resources such as the on-demand instances in Amazon EC2. While dynamically priced cloud resources enable to maximize datacenter resource utilization and minimize cost for the consumers, unfortunately, such auction mechanisms achieve these benefits only at a cost significant of private information leakage. In an auction-based mechanism, the private information includes information on the demands of the consumers that can lead an attacker to understand the current computing requirements of the consumers and perhaps even allow the inference of the workload patterns of the consumers. In this paper, we propose PADS, a strategy-proof differentially private auction mechanism that allows cloud providers to privately trade resources with cloud consumers in such a way that individual bidding information of the cloud consumers is not exposed by the auction mechanism. We demonstrate that PADS achieves differential privacy and approximate truthfulness guarantees while maintaining good performance in terms of revenue gains and allocation efficiency. We evaluate PADS through extensive simulation experiments that demonstrate that in comparison to traditional auction mechanisms, PADS achieves relatively high revenues for cloud providers while guaranteeing the privacy of the participating consumers
Differential Privacy-Based Online Allocations towards Integrating Blockchain and Edge Computing
In recent years, the blockchain-based Internet of Things (IoT) has been
researched and applied widely, where each IoT device can act as a node in the
blockchain. However, these lightweight nodes usually do not have enough
computing power to complete the consensus or other computing-required tasks.
Edge computing network gives a platform to provide computing power to IoT
devices. A fundamental problem is how to allocate limited edge servers to IoT
devices in a highly untrustworthy environment. In a fair competition
environment, the allocation mechanism should be online, truthful, and privacy
safe. To address these three challenges, we propose an online multi-item double
auction (MIDA) mechanism, where IoT devices are buyers and edge servers are
sellers. In order to achieve the truthfulness, the participants' private
information is at risk of being exposed by inference attack, which may lead to
malicious manipulation of the market by adversaries. Then, we improve our MIDA
mechanism based on differential privacy to protect sensitive information from
being leaked. It interferes with the auction results slightly but guarantees
privacy protection with high confidence. Besides, we upgrade our
privacy-preserving MIDA mechanism such that adapting to more complex and
realistic scenarios. In the end, the effectiveness and correctness of
algorithms are evaluated and verified by theoretical analysis and numerical
simulations
Differentially Private Diffusion Auction: The Single-unit Case
Diffusion auction refers to an emerging paradigm of online marketplace where
an auctioneer utilises a social network to attract potential buyers. Diffusion
auction poses significant privacy risks. From the auction outcome, it is
possible to infer hidden, and potentially sensitive, preferences of buyers. To
mitigate such risks, we initiate the study of differential privacy (DP) in
diffusion auction mechanisms. DP is a well-established notion of privacy that
protects a system against inference attacks. Achieving DP in diffusion auctions
is non-trivial as the well-designed auction rules are required to incentivise
the buyers to truthfully report their neighbourhood. We study the single-unit
case and design two differentially private diffusion mechanisms (DPDMs):
recursive DPDM and layered DPDM. We prove that these mechanisms guarantee
differential privacy, incentive compatibility and individual rationality for
both valuations and neighbourhood. We then empirically compare their
performance on real and synthetic datasets
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
- …