Differentially private spectrum auction with approximate revenue maximization

Many spectrum auction mechanisms have been proposed for spectrum allocation problem, and unfortunately, few of them protect the bid privacy of bidders and achieve good social efficiency. In this paper, we propose PPS, a Privacy Preserving Strategyproof spectrum auction framework. We design two schemes based on PPS separately for 1) the single-unit auction model (SUA), where only single channel will be sold in the spectrum market; and 2) the multi-unit auction model (MUA), where the primary user subleases multi-unit channels to the secondary users and each of the secondary users wants to access multi-unit channels either. Since the social efficiency maximization problem is NP-hard in both auction models, we present allocation mechanisms with approximation factors of (1 + ε) and 32 separately for SUA and MUA, and further judiciously design strategyproof auction mechanisms with privacy preserving based on them. Our extensive evaluations show that our mechanisms achieve good social efficiency and with low computation and communication overhead.

In cellular networks, a recent trend in research is to make spectrum access dynamic in the spatial and temporal dimensions for the sake of efficient utilization of spectrum. In one such model, the spectrum is divided into channels and periodically allocated to competing base stations using an auction-based market mechanism. An “efficient” auction mechanism is essential to the success of such a dynamic spectrum access model. A key objective in designing an auction mechanism is “truthfulness.” Combining this objective with an optimization of some social choice function (such as the social-welfare or the generated revenue) is highly desirable. In this paper, we design polynomial-time spectrum auction mechanisms that are truthful and yield an allocation with O(1)-approximate social-welfare or revenue. Our mechanisms generalize to general interference models. To the best of our knowledge, ours is the first work to design polynomial-time truthful spectrum auction mechanisms with a constant-factor approximation of either the expected revenue or the social-welfare. We demonstrate the performance of our designed mechanism through simulations.

Auctions are believed to be effective methods to solve the problem of wireless spectrum allocation. Existing spectrum auction mechanisms are all centralized and suffer from several critical drawbacks of the centralized systems, which motivates the design of distributed spectrum auction mechanisms. However, extending a centralized spectrum auction to a distributed one broadens the strategy space of agents from one dimension (bid) to three dimensions (bid, communication, and computation), and thus cannot be solved by traditional approaches from mechanism design. In this paper, we propose two distributed spectrum auction mechanisms, namely distributed VCG and FAITH. Distributed VCG implements the celebrated Vickrey-Clarke-Groves mechanism in a distributed fashion to achieve optimal social welfare, at the cost of exponential communication overhead. In contrast, FAITH achieves sub-optimal social welfare with tractable computation and communication overhead. We prove that both of the two proposed mechanisms achieve faithfulness, i.e., the agents' individual utilities are maximized, if they follow the intended strategies. Besides, we extend FAITH to adapt to dynamic scenarios where agents can arrive or depart at any time, without violating the property of faithfulness. We implement distributed VCG and FAITH, and evaluate their performance in various setups. Evaluation results show that distributed VCG results in optimal allocation, while FAITH is more efficient in computation and communication.

The demand for mobile services is growing aberrantly, which provides both challenges and opportunities for wireless networks. Wireless network virtualization is suggested as a key progression path for enhancing the capacity and resource utilization in the forthcoming fifth-generation mobile networks. In this paper, a software-defined network (SDN) enabled cloud radio access network (C-RAN) framework is proposed for enabling spectrum auction with a two-tier architecture support. In Tier-I, several remote radio heads (RRHs) are introduced to act as the secondary service providers to provide services to its small cell users (SUEs) by exploiting the purchased underutilized or idle resources from the primary service provider in Tier-II. Specifically, in order to maintain quality-of-service requirements of the SUEs, the revenue maximization problem for the RRHs is formulated by considering the user association, band assignment, interference management, and budget allowance. In Tier-II, an SDN-enabled spectrum auction mechanism is proposed for maximizing the social welfare based on the SUEs’ service requirements of all participating RRHs from Tier-I. In this auction mechanism, the bipartite graph is utilized to determine the socially optimal winners and the price charging scheme is proposed inspired by the well-known Vickrey–Clarke–Groves auction method. Simulation results reveal the performance and the benefits of the proposed SDN-enabled spectrum auction mechanism under different scenarios.

In recent years, a large number of spectrum mechanisms have been proposed, but these mechanisms ignore the security issues that arise during the design of the mechanism. In this paper, two secure models for sealed-bid spectrum auction are given based on Wang’s generic spectrum auction mechanism. One is the basic model and another improved model based on the basic model is proposed, which maximizes Social welfare while it is a Privacy-preserving Spectrum auction mechanism with public Verification namely SPSV. The SPSV scheme achieves the properties of maximizing the social welfare but also, by using the double paillier cryptosystem, it is privacy-preserving for bidders’ bids without revealing any sensitive information to auctioneer or agent during the entire spectrum auction. Oblivious transfer is applied to ensure the anonymity of bidders. Furthermore, the use of inequality comparison proof also provides the public verification of winner group to verify the comparison relationship between winner groups and losing groups. At last, the performance analysis are given.

Cognitive radio technology uses the ability of dynamic spectrum access to solve the problem of spectrum efficiency, but considered the view of application, the designs depended cognitive radio technology face the hardware restriction, and the cost is too expensive. Therefore the spectrum auction mechanism referenced to economic theory has a high value of application; the theory is a hot issue. However, existing spectrum auction mechanisms have faced with the problem of NP complexity, and also can not meet the demand of real-time. Based on the wireless spectrum auction theory, we have proposed an improved scheme of pricing mechanism by reverse algorithm.

In recent years, auction theory has been extensively studied and many state-of-art solutions have been proposed aiming at allocating scarce resources (e.g. spectrum resources in wireless communications). Unfortunately, most of these studies assume that the auctioneer is always trustworthy in the sealed-bid auctions, which is not always true in a more realistic scenario. On the other hand, performance guarantee, such as social efficiency maximization, is also crucial for auction mechanism design. Therefore, the goal of this work is to design a series of strategyproof and privacy preserving auction mechanisms that maximize the social efficiency. To make the designed auction model more general, we allow the bidders to express their preferences about multiple items, which is often regarded as the multi-unit auction. As computing an optimal allocation in multi-unit auction is NP-hard, we design a set of near optimal allocation mechanisms with privacy preserving separately for: (1) The auction aims at identical multi-items trading; and (2) The auction aims at distinct multi-items trading, which is also known as combinatorial auction. To the best of our knowledge, we are the first to design strategyproof multi-unit auction mechanisms with privacy preserving, which maximize the social efficiency at the same time. The evaluation results corroborate our theoretical analysis, and show that our proposed methods achieve low computation and communication complexity.

Auction is an effective mechanism to distribute spectrum resources. Although many privacy-preserving auction schemes for spectrum allocation have been proposed, none of them is able to perform practical spectrum auctions while ensuring enough security for bidders' private information, such as geo-locations, bid values, and data access patterns. To address this problem, we propose SLISA, a lightweight auction framework which enables an efficient spectrum allocation without revealing anything but the auction outcome, i.e., the winning bidders and their clearing prices. We present contributions on two fronts. First, as a foundation of our design, we adopt a Shuffle-then-Compute strategy to build a series of secure sub-protocols based on lightweight cryptographic primitives (e.g., additive secret sharing and basic garbled circuits). Second, we improve an advanced spectrum auction mechanism to make it data-oblivious, such that data access patterns can be hidden. Meanwhile, the modified protocols adapt to our elaborate building blocks without affecting its validity and security. We formally prove the security of all protocols under a semi-honest adversary model, and demonstrate performance improvements compared with state-of-the-art works through extensive experiments.

Dynamic spectrum reallocation, under which the spectrum owners temporarily share the underutilized spectrum to secondary users for economic profit, is an important approach to improve the spectrum utilization ratio. Auction is believed to be a natural marketing tool to incentivize the spectrum owners, and thus redistribute the idle spectrum efficiently. Extensive researches have been done in the problem of truthful spectrum auction, in which the bidders bid based on their true valuations of the spectrum. The true valuation of the individual bidder, however, is a private information which should be protected against exposure. In this paper, we propose a privacy preserving truthful spectrum auction scheme by utilizing homomorphic encryption. The proposed scheme reveals the group bids but hides the users' bids even from the auctioneer. The evaluation results show that the proposed scheme achieves good spectrum utilization efficiency with low communication and computation overheads.

In this paper, we propose a secure data downloading protocol with privacy preservation in vehicular ad hoc networks (VANETs). In the data downloading application, vehicles send requests, such as where is the nearest gas station, at a road side unit (RSU) and receive the corresponding responses from application servers via current or the following RSUs. It would be easy for eavesdroppers to get vehicles' private information if semi-trust RSUs are compromised because both request and response messages are forwarded by them. Therefore, we develop a protocol which enables vehicles to download data securely from RSUs with their privacy under protection even when one or multiple RSUs are compromised. Our protocol guarantees vehicles exclusive access to their requested data while eavesdroppers can not obtain any private information of the vehicles. Possible attacks and the corresponding solutions as well as privacy evaluation are discussed to demonstrate the performance of the proposed protocol.

In this paper, we investigate the problem of recall mechanism in a dynamic spectrum auction system. The licensed spectrum holders (SHs) can auction their idle channels to the secondary wireless service providers (SWSPs) for economic incentive. However, the primary user (PU) of an SH has higher priority than the SWSPs for the spectrum access. To deal with a sudden increase in PUs' spectrum demands after auction and guarantee their quality of services (QoS), we propose a Local recall-based dYnamic double spectRum auctIon meChanism (LYRIC) for SHs' spectrum auction operation. Different from the most existing approaches about spectrum recall-based spectrum auction, the proposed spectrum auction method for heterogeneous spectrum transaction takes account of the joint effect of heterogeneous demand, spectrum spatial reusability, and the economic efficiency. Simulation results show that the proposed LYRIC algorithm can improve the auction process of SHs, the channels utilization efficiency and the average buyers' satisfactory level with guarantees on the QoS of PUs.

Interdependent Value Multi-unit Auctions for Initial Allocation of Emission Permits

One of major concerns for China and developing countries is the new international trade rule concerning carbon emissions, which one needs to know its suitable mechanism and the possible implication on China's carbon trading market. Precisely, in an emission‐and‐trade scheme, an important question is whether the auction outcome is efficient, since this is closely related with the economic costs of emissions reduction. Due to the unique features of carbon permits, existing auction mechanisms do not seem to be suitable for their distribution. Inspired by the tâtonnement process, this paper proposes a new auction mechanism for them. For both practical application and theoretical extension, the discrete and continuous situations are examined, respectively. The result shows that the proposed auction converges to Pareto optimal equilibria, and that without ties, the equilibrium is unique. For bidders, the straightforward bidding strategy is the most preferred one, in the sense that the strategy makes them avoid the risk of loss. Intuitively, the auctioneer's utility is increasing in the number of bidders, while bidders’ pay‐off is increasing in permits. To a certain extent, the proposed auction mechanism resolves the confliction between efficiency and revenue in the multi‐item auction theory, while it increases people's understanding for carbon emission auction mechanism design.

In the age of the Internet of Things (IoT), an immense volume of data is rapidly generated and entrusted to external units, including cloud centers for storage and analysis. This implies that the data produced by IoT devices is conveyed and stored in a distant location, where it can undergo processing, examination, and utilization to extract valuable insights and facilitate informed decision-making. Privacy protection methods can be applied in IoT settings to safeguard sensitive and private information from potential security risks. Within the scope of IoT privacy, data sanitization involves the permanent and irreversible concealment of all sensitive data from extensive streams of information. Conversely, data restoration is the reverse process of data sanitization. Considering privacy preservation in IoT for data streaming, this research paper implements a Deep Learning (DL) tuned key-based data sanitization and restoration approach. The entire process of the implemented approach is handled by two significant steps such as data sanitization and data restoration. In the data sanitization step, three processes take place such as retrieval of features, key generation via Self-Adaptive Osprey Optimization (SAOO) and key tuning via Enhanced Convolutional Neural Network (ECNN). Initially, features such as raw features, statistical features and improved entropy-based features are retrieved (considering this as sensitive information. To sanitize this, key generation is the subsequent phase, where optimal keys are generated by SAOO and its security and confidentiality are ensured by the Khatri–Rao product. Then, these optimal keys are tuned by ECNN to ensure the efficacy of privacy preservation in IoT without compromising the performance of the model. Finally, the data restoration process is to recover the previously sanitized data in an IoT environment and reconstitute the original data from their sanitized form while maintaining the security and confidentiality of raw sensitive data. With these steps, the privacy of an IoT environment is protected from any form of issues. Additionally, this approach is experimentally explored to determine its efficacy using various analyses.

Mobile social network (MSN) emerges as a promising social network paradigm that enables mobile users' information sharing in the proximity and facilitates their cyber-physical-social interactions. As the advertisements, rumors, and spams spread in MSNs, it is necessary to filter spams before they arrive at the recipients to make the MSN energy efficient. To this end, we propose a personalized fine-grained filtering scheme (PIF) with privacy preservation in MSNs. Specifically, we first develop a social-assisted filter distribution scheme, where the filter creators send filters to their social friends (i.e., filter holders). These fil- ter holders store filters and decide to block spams or relay the desired packets through coarse-grained and fine-grained keyword filtering schemes. Meanwhile, the developed cryptographic filter- ing schemes protect creator's private information (i.e., keyword) embedded in the filters from directly disclosing to other users. In addition, we establish a Merkle Hash tree to store filters as leaf nodes where filter creators can check if the distributed filters need to be updated by retrieving the value of root node. It is demon- strated that the PIF can protect users' private keywords included in the filter from disclosure to others and detect forged filters. We also conduct the trace-driven simulations to show that the PIF can not only filter spams efficiently but also achieve high delivery ratio and low latency with acceptable resource consumption.