arXiv subjects

Junshan Zhang

Publications and source records attributed to Junshan Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2020

Privacy-aware Data Trading

The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always hold true in reality, whereas the latter approach reduces the value of data. In this paper, we investigate the privacy leakage issue from the root source. Specifically, we take a fresh look to reverse the inferior position of the data provider by making her dominate the game with the collector to solve the dilemma in data trading. To that aim, we propose the noisy-sequentially zero-determinant (NSZD) strategies by tailoring the classical zero-determinant strategies, originally designed for the simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies can empower the data provider to unilaterally set the expected payoff of the data collector or enforce a positive relationship between her and the data collector's expected payoffs. Both strategies can stimulate a rational data collector to behave honestly, boosting a healthy data trading market. Numerical simulations are used to examine the impacts of key parameters and the feasible region where the data provider can be an NSZD player. Finally, we prove that the data collector cannot employ NSZD to further dominate the data market for deteriorating privacy leakage.

cs.GT↗

arXiv2019

Hopping-Proof and Fee-Free Pooled Mining in Blockchain

The pool-hopping attack casts down the expected profits of both the mining pool and honest miners in Blockchain. The mainstream countermeasures, namely PPS (pay-per-share) and PPLNS (pay-per-last-N-share), can hedge pool hopping, but pose a risk to the pool as well as the cost to miners. In this study, we apply the zero-determinant (ZD) theory to design a novel pooled mining which offers an incentive mechanism for motivating non-memorial and memorial evolutionary miners not to switch in pools strategically. In short, our hopping-proof pooled mining has three unique features: 1) fee-free. No fee is charged if the miner does not hop. 2) wide applicability. It can be employed in both prepaid and postpaid mechanisms. 3) fairness. Even the pool can dominate the game with any miner, he has to cooperate when the miner does not hop among pools. The fairness of our scheme makes it have long-term sustainability. To the best of our knowledge, we are the first to propose a hopping-proof pooled mining with the above three natures simultaneously. Both theoretical and experimental analyses demonstrate the effectiveness of our scheme.

cs.GT↗

arXiv2019

Preserving privacy enables "co-existence equilibrium" of competitive diffusion in social networks

With the advent of social media, different companies often promote competing products simultaneously for word-of-mouth diffusion and adoption by users in social networks. For such scenarios of competitive diffusion, prior studies show that the weaker product will soon become extinct (i.e., "winner takes all"). It is intriguing to observe that in practice, however, competing products, such as iPhone and Android phone, often coexist in the market. This discrepancy may result from many factors such as the phenomenon that a user in the real world may not spread its use of a product due to dissatisfaction of the product or privacy protection. In this paper, we incorporate users' privacy for spreading behavior into competitive diffusion of two products and develop a problem formulation for privacy-aware competitive diffusion. Then, we prove that privacy-preserving mechanisms can enable a "coexistence equilibrium" (i.e., two competing products coexist in the equilibrium) in competitive diffusion over social networks. In addition to the rigorous analysis, we also demonstrate our results with experiments over real network topologies.

cs.SI↗

arXiv2019

Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing

With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions Bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of interest. However, research on edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence. Specifically, we first review the background and motivation for artificial intelligence running at the network edge. We then provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training/inference at the network edge. Finally, we discuss future research opportunities on edge intelligence. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions and inspire further research ideas on edge intelligence.

cs.DC↗

arXiv2019

Impact of Social Learning on Privacy-Preserving Data Collection

We study a model where a data collector obtains data from users through a payment mechanism, aiming to learn the underlying state from the elicited data. The private signal of each user represents her knowledge about the state; and through social interactions each user can also learn noisy versions of her social friends' signals, which is called `learned group signals'. Thanks to social learning, users have richer information about the state beyond their private signals. Based on both her private signal and learned group signals, each user makes strategic decisions to report a privacy-preserved version of her data to the data collector. We develop a Bayesian game theoretic framework to study the impact of social learning on users' data reporting strategies and devise the payment mechanism for the data collector accordingly. Our findings reveal that, in general, the desired data reporting strategy at the Bayesian-Nash equilibrium can be in the form of either a symmetric randomized response (SR) strategy or an informative non-disclosive (ND) strategy. Specifically, a generalized majority voting rule is applied by each user to her noisy group signals to determine which strategy to follow. Further, when a user plays the ND strategy, she reports privacy-preserving data completely based on her group signals, independent of her private signal, which indicates that her privacy cost is zero. We emphasize that the reported data when a user plays the ND strategy is still informative about the underlying state because it is based on her learned group signals. As a result, both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation at a given payment budget. We further derive bounds on the minimum total payment required to achieve a given level of state estimation accuracy.

cs.GT↗

arXiv2019

A Tensor-Based Framework for Studying Eigenvector Multicentrality in Multilayer Networks

Centrality is widely recognized as one of the most critical measures to provide insight in the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.

physics.soc-ph↗

arXiv2018

Content Popularity Prediction Towards Location-Aware Mobile Edge Caching

Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an $H_{\infty}$ filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.

cs.NI↗

arXiv2018

Mobile Social Services with Network Externality: From Separate Pricing to Bundled Pricing

Today, many wireless device providers choose to sell devices bundled with complementary mobile social services, which exhibit strong positive network externality. This paper aims to quantify the benefits of selling devices and complementary services under the following three strategies: separate pricing, bundled pricing, and hybrid pricing (both the separate and bundled options are offered). A comprehensive comparison of the above three strategies is carried out for two popular service models, namely physical connectivity sharing and virtual content sharing, respectively. We first study the physical service model where the provider (e.g., FON) offers users customized WiFi devices for indoor Internet access, and allows service subscribers to physically access all device owners' WiFi when traveling. Observing that all device-owners contribute to the connectivity sharing, we show, via a Stackelberg game theoretic approach, that bundled pricing outperforms separate pricing as long as the total cost of device and service is reasonably low to stimulate network externality. Further, hybrid pricing strictly dominates bundled pricing thanks to the pricing flexibility to keep high marginal profit of device-selling. Next, we investigate the virtual sharing service model where the provider (e.g., Apple) sells devices and device-supported applications. Different from the connectivity service model, in this model service subscribers directly contribute to the virtual content sharing, and the network externality can be fairly strong. We prove that hybrid pricing degenerates to bundled pricing if the network externality degree is larger than the average device valuation, which is in stark contrast with the connectivity service model in which hybrid pricing always outperforms bundled pricing.

cs.GT↗

arXiv2017

Mobile Edge Caching: An Optimal Auction Approach

With the explosive growth of wireless data, the sheer size of the mobile traffic is challenging the capacity of current wireless systems. To tackle this challenge, mobile edge caching has emerged as a promising paradigm recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality.

cs.NI↗

arXiv2017

REAP: An Efficient Incentive Mechanism for Reconciling Aggregation Accuracy and Individual Privacy in Crowdsensing

Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies on co-design of incentive mechanism and privacy preservation assume a trustworthy fusion center (FC). Very recent work has taken steps to relax the assumption on trustworthy FC and allows participatory users (PUs) to add well calibrated noise to their raw sensing data before reporting them, whereas the focus is on the equilibrium behavior of data subjects with binary data. Making a paradigm shift, this paper aim to quantify the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer adding larger noise for higher privacy-preserving levels (PPLs). To achieve a good balance therein, we design an efficient incentive mechanism to REconcile FC's Aggregation accuracy and individual PU's data Privacy (REAP). Specifically, we adopt the celebrated notion of differential privacy to measure PUs' PPLs and quantify their impacts on FC's aggregation accuracy. Then, appealing to Contract Theory, we design an incentive mechanism to maximize FC's aggregation accuracy under a given budget. The proposed incentive mechanism offers different contracts to PUs with different privacy preferences, by which FC can directly control PUs. It can further overcome the information asymmetry, i.e., the FC typically does not know each PU's precise privacy preference. We derive closed-form solutions for the optimal contracts in both complete information and incomplete information scenarios. Further, the results are generalized to the continuous case where PUs' privacy preferences take values in a continuous domain. Extensive simulations are provided to validate the feasibility and advantages of our proposed incentive mechanism.

cs.GT↗

arXiv2017

Dynamic Mobile Edge Caching with Location Differentiation

Mobile edge caching enables content delivery directly within the radio access network, which effectively alleviates the backhaul burden and reduces round-trip latency. To fully exploit the edge resources, the most popular contents should be identified and cached. Observing that content popularity varies greatly at different locations, to maximize local hit rate, this paper proposes an online learning algorithm that dynamically predicts content hit rate, and makes location-differentiated caching decisions. Specifically, a linear model is used to estimate the future hit rate. Considering the variations in user demand, a perturbation is added to the estimation to account for uncertainty. The proposed learning algorithm requires no training phase, and hence is adaptive to the time-varying content popularity profile. Theoretical analysis indicates that the proposed algorithm asymptotically approaches the optimal policy in the long term. Extensive simulations based on real world traces show that, the proposed algorithm achieves higher hit rate and better adaptiveness to content popularity fluctuation, compared with other schemes.

cs.NI↗

arXiv2016

Amazon in the White Space: Social Recommendation Aided Distributed Spectrum Access

Distributed spectrum access (DSA) is challenging since an individual secondary user often has limited sensing capabilities only. One key insight is that channel recommendation among secondary users can help to take advantage of the inherent correlation structure of spectrum availability in both time and space, and enable users to obtain more informed spectrum opportunities. With this insight, we advocate to leverage the wisdom of crowds, and devise social recommendation aided DSA mechanisms to orient secondary users to make more intelligent spectrum access decisions, for both strong and weak network information cases. We start with the strong network information case where secondary users have the statistical information. To mitigate the difficulty due to the curse of dimensionality in the stochastic game approach, we take the one-step Nash approach and cast the social recommendation aided DSA decision making problem at each time slot as a strategic game. We show that it is a potential game, and then devise an algorithm to achieve the Nash equilibrium by exploiting its finite improvement property. For the weak information case where secondary users do not have the statistical information, we develop a distributed reinforcement learning mechanism for social recommendation aided DSA based on the local observations of secondary users only. Appealing to the maximum-norm contraction mapping, we also derive the conditions under which the distributed mechanism converges and characterize the equilibrium therein. Numerical results reveal that the proposed social recommendation aided DSA mechanisms can achieve superior performance using real social data traces and its performance loss in the weak network information case is insignificant, compared with the strong network information case.

cs.NI↗

arXiv2016

Exploiting Social Tie Structure for Cooperative Wireless Networking: A Social Group Utility Maximization Framework

In this paper, we develop a social group utility maximization (SGUM) framework for cooperative wireless networking that takes into account both social relationships and physical coupling among users. We show that this framework provides rich modeling flexibility and spans the continuum between non-cooperative game and network utility maximization (NUM) -- two traditionally disjoint paradigms for network optimization. Based on this framework, we study three important applications of SGUM, in database assisted spectrum access, power control, and random access control, respectively. For the case of database assisted spectrum access, we show that the SGUM game is a potential game and always admits a socially-aware Nash equilibrium (SNE). We develop a randomized distributed spectrum access algorithm that can asymptotically converge to the optimal SNE, derive upper bounds on the convergence time, and also quantify the trade-off between the performance and convergence time of the algorithm. We further show that the performance gap of SNE by the algorithm from the NUM solution decreases as the strength of social ties among users increases and the performance gap is zero when the strengths of social ties among users reach the maximum values. For the cases of power control and random access control, we show that there exists a unique SNE. Furthermore, as the strength of social ties increases from the minimum to the maximum, a player's SNE strategy migrates from the Nash equilibrium strategy in a standard non-cooperative game to the socially-optimal strategy in network utility maximization. Furthermore, we show that the SGUM framework can be generalized to take into account both positive and negative social ties among users and can be a useful tool for studying network security problems.

cs.NI↗

arXiv2016

The Value of Privacy: Strategic Data Subjects, Incentive Mechanisms and Fundamental Limits

We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of $ε$ units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of $ε$. The higher $ε$ is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy $ε$ units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum.

cs.GT↗

arXiv2015

On the Relation Between Identifiability, Differential Privacy and Mutual-Information Privacy

This paper investigates the relation between three different notions of privacy: identifiability, differential privacy and mutual-information privacy. Under a unified privacy-distortion framework, where the distortion is defined to be the Hamming distance of the input and output databases, we establish some fundamental connections between these three privacy notions. Given a distortion level $D$, define $ε_{\mathrm{i}}^*(D)$ to be the smallest (best) identifiability level, and $ε_{\mathrm{d}}^*(D)$ to be the smallest differential privacy level. We characterize $ε_{\mathrm{i}}^*(D)$ and $ε_{\mathrm{d}}^*(D)$, and prove that $ε_{\mathrm{i}}^*(D)-ε_X\leε_{\mathrm{d}}^*(D)\leε_{\mathrm{i}}^*(D)$ for $D$ in some range, where $ε_X$ is a constant depending on the distribution of the original database $X$, and diminishes to zero when the distribution of $X$ is uniform. Furthermore, we show that identifiability and mutual-information privacy are consistent in the sense that given distortion level $D$, the mechanism that optimizes the mutual-information privacy also minimizes the identifiability level.

cs.CR↗

arXiv2015

Exploiting Social Trust Assisted Reciprocity (STAR) towards Utility-Optimal Socially-aware Crowdsensing

Mobile crowdsensing takes advantage of pervasive mobile devices to collect and process data for a variety of applications (e.g., traffic monitoring, spectrum sensing). In this study, a socially-aware crowdsensing system is advocated, in which a cloud-based platform incentivizes mobile users to participate in sensing tasks} by leveraging social trust among users, upon receiving sensing requests. For this system, social trust assisted reciprocity (STAR) - a synergistic marriage of social trust and reciprocity, is exploited to design an incentive mechanism that stimulates users' participation. Given the social trust structure among users, the efficacy of STAR for satisfying users' sensing requests is thoroughly investigated. Specifically, it is first shown that all requests can be satisfied if and only if sufficient social credit can be "transferred" from users who request more sensing service than they can provide to users who can provide more than they request. Then utility maximization for sensing services under STAR is investigated, and it is shown that it boils down to maximizing the utility of a circulation flow in the combined social graph and request graph. Accordingly, an algorithm that iteratively cancels a cycle of positive weight in the residual graph is developed, which computes the optimal solution efficiently, for both cases of divisible and indivisible sensing service. Extensive simulation results corroborate that STAR can significantly outperform the mechanisms using social trust only or reciprocity only.

cs.NI↗

arXiv2015

Distributed Opportunistic Scheduling for Energy Harvesting Based Wireless Networks: A Two-Stage Probing Approach

This paper considers a heterogeneous ad hoc network with multiple transmitter-receiver pairs, in which all transmitters are capable of harvesting renewable energy from the environment and compete for one shared channel by random access. In particular, we focus on two different scenarios: the constant energy harvesting (EH) rate model where the EH rate remains constant within the time of interest and the i.i.d. EH rate model where the EH rates are independent and identically distributed across different contention slots. To quantify the roles of both the energy state information (ESI) and the channel state information (CSI), a distributed opportunistic scheduling (DOS) framework with two-stage probing and save-then-transmit energy utilization is proposed. Then, the optimal throughput and the optimal scheduling strategy are obtained via one-dimension search, i.e., an iterative algorithm consisting of the following two steps in each iteration: First, assuming that the stored energy level at each transmitter is stationary with a given distribution, the expected throughput maximization problem is formulated as an optimal stopping problem, whose solution is proved to exist and then derived for both models; second, for a fixed stopping rule, the energy level at each transmitter is shown to be stationary and an efficient iterative algorithm is proposed to compute its steady-state distribution. Finally, we validate our analysis by numerical results and quantify the throughput gain compared with the best-effort delivery scheme.

cs.IT↗

arXiv2014

A Minimax Distortion View of Differentially Private Query Release

We consider the problem of differentially private query release through a synthetic database approach. Departing from the existing approaches that require the query set to be specified in advance, we advocate to devise query-set independent mechanisms, with an ambitious goal of providing accurate answers, while meeting the privacy constraints, for all queries in a general query class. Specifically, a differentially private mechanism is constructed to "encode" rich stochastic structure into the synthetic database, and "customized" companion estimators are then derived to provide accurate answers by making use of all available information, including the mechanism (which is public information) and the query functions. Accordingly, the distortion under the best of this kind of mechanisms at the worst-case query in a general query class, so called the minimax distortion, provides a fundamental characterization of differentially private query release. For the general class of statistical queries, we prove that with the squared-error distortion measure, the minimax distortion is $O(1/n)$ by deriving asymptotically tight upper and lower bounds in the regime that the database size $n$ goes to infinity. The upper bound is achievable by a mechanism $\mathcal{E}$ and its corresponding companion estimators, which points directly to the feasibility of the proposed approach in large databases. We further evaluate the mechanism $\mathcal{E}$ and the companion estimators through experiments on real datasets from Netflix and Facebook. Experimental results show improvement over the state-of-art MWEM algorithm and verify the scaling behavior $O(1/n)$ of the minimax distortion.

cs.CR↗