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

VPT: Privacy Preserving Energy Trading and Block Mining Mechanism for Blockchain based Virtual Power Plants

The desire to overcome reliability issues of distributed energy resources (DERs) lead researchers to development of a novel concept named as virtual power plant (VPP). VPPs are supposed to carry out intelligent, secure, and smart energy trading among prosumers, buyers, and generating stations along with providing efficient energy management. Therefore, integrating blockchain in decentralized VPP network emerged out as a new paradigm, and recent experiments over this integration have shown fruitful results. However, this decentralization also suffers with energy management, trust, reliability, and efficiency issues due to the dynamic nature of DERs. In order to overcome this, in this paper, we first work over providing efficient energy management strategy for VPP to enhance demand response, then we propose an energy oriented trading and block mining protocol and named it as proof of energy market (PoEM). To enhance it further, we integrate differential privacy in PoEM and propose a Private PoEM (PPoEM) model. Collectively, we propose a private decentralized VPP trading model and named it as Virtual Private Trading (VPT) model. We further carry out extensive theoretical analysis and derive step-by-step valuations for market race probability, market stability probability, energy trading expectation, winning state probability, and prospective leading time profit values. Afterwards, we carry out simulation-based experiment of our proposed model. The performance evaluation and theoretical analysis of our VPT model make it one of the most viable model for blockchain based VPP network as compared to other state-of-the-art works.Comment: Article Submitted for Revie

Security and Privacy in Dynamic Spectrum Access: Challenges and Solutions

abstract: Dynamic spectrum access (DSA) has great potential to address worldwide spectrum shortage by enhancing spectrum efficiency. It allows unlicensed secondary users to access the under-utilized spectrum when the primary users are not transmitting. On the other hand, the open wireless medium subjects DSA systems to various security and privacy issues, which might hinder the practical deployment. This dissertation consists of two parts to discuss the potential challenges and solutions. The first part consists of three chapters, with a focus on secondary-user authentication. Chapter One gives an overview of the challenges and existing solutions in spectrum-misuse detection. Chapter Two presents SpecGuard, the first crowdsourced spectrum-misuse detection framework for DSA systems. In SpecGuard, three novel schemes are proposed for embedding and detecting a spectrum permit at the physical layer. Chapter Three proposes SafeDSA, a novel PHY-based scheme utilizing temporal features for authenticating secondary users. In SafeDSA, the secondary user embeds his spectrum authorization into the cyclic prefix of each physical-layer symbol, which can be detected and authenticated by a verifier. The second part also consists of three chapters, with a focus on crowdsourced spectrum sensing (CSS) with privacy consideration. CSS allows a spectrum sensing provider (SSP) to outsource the spectrum sensing to distributed mobile users. Without strong incentives and location-privacy protection in place, however, mobile users are reluctant to act as crowdsourcing workers for spectrum-sensing tasks. Chapter Four gives an overview of the challenges and existing solutions. Chapter Five presents PriCSS, where the SSP selects participants based on the exponential mechanism such that the participants' sensing cost, associated with their locations, are privacy-preserved. Chapter Six further proposes DPSense, a framework that allows the honest-but-curious SSP to select mobile users for executing spatiotemporal spectrum-sensing tasks without violating the location privacy of mobile users. By collecting perturbed location traces with differential privacy guarantee from participants, the SSP assigns spectrum-sensing tasks to participants with the consideration of both spatial and temporal factors. Through theoretical analysis and simulations, the efficacy and effectiveness of the proposed schemes are validated.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201