arXiv subjects

Jun Zhao

Publications and source records attributed to Jun Zhao.

At least 469 records · Page 26Linked to original sources

arXiv2019

PandaX limits on light dark matter with light mediator in the singlet extension of MSSM

Using the latest PandaX limits on light dark matter (DM) with light mediator, we check the implication on the parameter space of the general singlet extension of MSSM (without $Z_3$ symmetry), which can have a sizable DM self-interaction to solve the small-scale structure problem. We find that the PandaX limits can stringently constrain such a paramter space, depending on the coupling $λ$ between the singlet and doublet Higgs fields. For the singlet extension of MSSM with $Z_3$ symmetry, the so-called NMSSM, we also demonstrate the PandaX constraints on its parameter space which gives a light DM with correct relic density but without sufficient self-interaction to solve the small-scale structure problem. We find that in this NMSSM the GeV dark matter with a sub-GeV mediator has been stringently constrained.

hep-ph↗

arXiv2019

An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning

With the advent of the era of big data, deep learning has become a prevalent building block in a variety of machine learning or data mining tasks, such as signal processing, network modeling and traffic analysis, to name a few. The massive user data crowdsourced plays a crucial role in the success of deep learning models. However, it has been shown that user data may be inferred from trained neural models and thereby exposed to potential adversaries, which raises information security and privacy concerns. To address this issue, recent studies leverage the technique of differential privacy to design private-preserving deep learning algorithms. Albeit successful at privacy protection, differential privacy degrades the performance of neural models. In this paper, we develop ADADP, an adaptive and fast convergent learning algorithm with a provable privacy guarantee. ADADP significantly reduces the privacy cost by improving the convergence speed with an adaptive learning rate and mitigates the negative effect of differential privacy upon the model accuracy by introducing adaptive noise. The performance of ADADP is evaluated on real-world datasets. Experiment results show that it outperforms state-of-the-art differentially private approaches in terms of both privacy cost and model accuracy.

cs.CR↗

arXiv2019

Neutron Spin Resonance in the Heavily Hole-doped KFe$_{2}$As$_{2}$ Superconductor

We report high-resolution neutron scattering measurements of the low energy spin fluctuations of KFe$_{2}$As$_{2}$, the end member of the hole-doped Ba$_{1-x}$K$_x$Fe$_2$As$_2$ family with only hole pockets, above and below its superconducting transition temperature $T_c$ ($\sim$ 3.5 K). Our data reveals clear spin fluctuations at the incommensurate wave vector ($0.5\pmδ$, 0, $L$), ($δ$ = 0.2)(1-Fe unit cell), which exhibit $L$-modulation peaking at $L=0.5$. Upon cooling to the superconducting state, the incommensurate spin fluctuations gradually open a spin-gap and form a sharp spin resonance mode. The incommensurability ($2δ$ = 0.4) of the resonance mode ($\sim1.2$ meV) is considerably larger than the previously reported value ($2δ$ $\approx0.32$) at higher energies ($\ge\sim6$ meV). The determination of the momentum structure of spin fluctuation in the low energy limit allows a direct comparison with the realistic Fermi surface and superconducting gap structure. Our results point to an $s$-wave pairing with a reversed sign between the hole pockets near the zone center in KFe$_{2}$As$_{2}$.

cond-mat.supr-con↗

arXiv2019

Reviewing and Improving the Gaussian Mechanism for Differential Privacy

Differential privacy provides a rigorous framework to quantify data privacy, and has received considerable interest recently. A randomized mechanism satisfying $(ε, δ)$-differential privacy (DP) roughly means that, except with a small probability $δ$, altering a record in a dataset cannot change the probability that an output is seen by more than a multiplicative factor $e^ε $. A well-known solution to $(ε, δ)$-DP is the Gaussian mechanism initiated by Dwork et al. [1] in 2006 with an improvement by Dwork and Roth [2] in 2014, where a Gaussian noise amount $\sqrt{2\ln \frac{2}δ} \times \fracΔε$ of [1] or $\sqrt{2\ln \frac{1.25}δ} \times \fracΔε$ of [2] is added independently to each dimension of the query result, for a query with $\ell_2$-sensitivity $Δ$. Although both classical Gaussian mechanisms [1,2] assume $0 < ε\leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $ε$ and $δ$ where the added noise amounts of [1,2] do not achieve $(ε,δ)$-DP. We obtain such result by analyzing the optimal noise amount $σ_{DP-OPT}$ for $(ε,δ)$-DP and identifying $ε$ and $δ$ where the noise amounts of classical mechanisms are even less than $σ_{DP-OPT}$. Since $σ_{DP-OPT}$ has no closed-form expression and needs to be approximated in an iterative manner, we propose Gaussian mechanisms by deriving closed-form upper bounds for $σ_{DP-OPT}$. Our mechanisms achieve $(ε,δ)$-DP for any $ε$, while the classical mechanisms [1,2] do not achieve $(ε,δ)$-DP for large $ε$ given $δ$. Moreover, the utilities of our mechanisms improve those of [1,2] and are close to that of the optimal yet more computationally expensive Gaussian mechanism.

cs.CR↗

arXiv2019

A Survey of Blockchain Applications in Different Domains

Blockchains have received much attention recently since they provide decentralized approaches to the creation and management of value. Many banks, Internet companies, car manufacturers, and even governments worldwide have incorporated or started considering blockchains to improve the security, scalability, and efficiency of their services. In this paper, we survey blockchain applications in different areas. These areas include cryptocurrency, healthcare, advertising, insurance, copyright protection, energy, and societal applications. Our work provides a timely summary for individuals and organizations interested in blockchains. We envision our study to motivate more blockchain applications.

cs.CR↗

arXiv2019

Testing electroweak SUSY for muon $g-2$ and dark matter at the LHC and beyond

Given that the LHC experiment has produced strong constraints on the colored supersymmetric particles (sparticles), testing the electroweak supersymmetry (EWSUSY) will be the next crucial task at the LHC. On the other hand, the light electroweakinos and sleptons in the EWSUSY can also contribute to the dark matter (DM) and low energy lepton observables. The precision measurements of them will provide the indirect evidence of SUSY. In this work, we confront the EWSUSY with the muon $g-2$ anomaly, the DM relic density, the direct detection limits and the latest LHC Run-2 data. We find that the sneutrino DM or the neutralino DM with sizable higgsino component has been excluded by the direct detections. Then two viable scenarios are pinned down: one has the light compressed bino and sleptons but heavy higgsinos, and the other has the light compressed bino, winos and sleptons. In the former case, the LSP and slepton masses have to be smaller than about 350 GeV. While in the latter case, the LSP and slepton masses have to be smaller than about 700 GeV and 800 GeV, respectively. From investigating the observability of these sparticles in both scenarios at future colliders, it turns out that the HE-LHC with a luminosity of 15 ab$^{-1}$ can exclude the whole BHL and most part of BWL scenarios at $2σ$ level. The precision measurement of the Higgs couplings at the lepton colliders could play a complementary role of probing the BWL scenario.

hep-ph↗

arXiv2019

A Survey of Intelligent Reflecting Surfaces (IRSs): Towards 6G Wireless Communication Networks

Intelligent reflecting surfaces (IRSs) tune wireless environments to increase spectrum and energy efficiencies. In view of much recent attention to the IRS concept as a promising technology for 6G wireless communications, we present a survey of IRSs in this paper. Specifically, we categorize recent research studies of IRSs as follows. For IRS-aided communications, the summary includes capacity/data rate analyses, power/spectral optimizations, channel estimation, deep learning-based design, and reliability analysis. Then we review IRSs implementations as well as the use of IRSs in secure communications, terminal-positioning, and other novel applications. We further identify future research directions for IRSs, with an envision of the IRS technology playing a critical role in 6G communication networks similar to that of massive MIMO in 5G networks. As a timely summary of IRSs, our work will be of interest to both researchers and practitioners working on IRSs for 6G networks.

eess.SP↗

arXiv2019

Relations among different privacy notions

We present a comprehensive view of the relations among several privacy notions: differential privacy (DP) [1], Bayesian differential privacy (BDP) [2], semantic privacy (SP) [3], and membership privacy (MP) [4]. The results are organized into two parts. In part one, we extend the notion of semantic privacy (SP) to Bayesian semantic privacy (BSP) and show its essential equivalence with Bayesian differential privacy (BDP) in the quantitative sense. We prove the relations between BDP, BSP, and SP as follows: $ε$-BDP $\Longleftarrow$ $\big(\frac{1}{2}-\frac{1}{e^ε+1}\big)$-BSP, and $ε$-BDP $\Longrightarrow$ $(e^{2ε}-1)$-BSP $\Longrightarrow$ $(e^{2ε}-1)$-SP. In addition, we obtain a minor result $ε$-DP $\Longleftarrow$ $\big(\frac{1}{2}-\frac{1}{e^ε+1}\big)$-SP, which improves the result of Kasiviswanathan and Smith [3] stating $ε$-DP $\Longleftarrow$ $ε/6$-SP for $ε\leq 1.35$. In part two, we establish the relations between BDP and MP. First, $ε$-BDP $\Longrightarrow$ $ε$-MP. Second, for a family of distributions that are downward scalable in the sense of Li et al. [4], it is shown that $ε$-BDP $\Longleftarrow$ $ε$-MP.

cs.CR↗

arXiv2019

Secure Connectivity of Wireless Sensor Networks Under Key Predistribution with on/off Channels

Security is an important issue in wireless sensor networks (WSNs), which are often deployed in hostile environments. The q-composite key predistribution scheme has been recognized as a suitable approach to secure WSNs. Although the q-composite scheme has received much attention in the literature, there is still a lack of rigorous analysis for secure WSNs operating under the q-composite scheme in consideration of the unreliability of links. One main difficulty lies in analyzing the network topology whose links are not independent. Wireless links can be unreliable in practice due to the presence of physical barriers between sensors or because of harsh environmental conditions severely impairing communications. In this paper, we resolve the difficult challenge and investigate k-connectivity in secure WSNs operating under the q-composite scheme with unreliable communication links modeled as independent on/off channels, where k-connectivity ensures connectivity despite the failure of any (k - 1) sensors or links, and connectivity means that any two sensors can find a path in between for secure communication. Specifically, we derive the asymptotically exact probability and a zero-one law for k-connectivity. We further use the theoretical results to provide design guidelines for secure WSNs. Experimental results also confirm the validity of our analytical findings.

cs.CR↗

arXiv2019

Composition Properties of Bayesian Differential Privacy

Differential privacy is a rigorous privacy standard that has been applied to a range of data analysis tasks. To broaden the application scenarios of differential privacy when data records have dependencies, the notion of Bayesian differential privacy has been recently proposed. However, it is unknown whether Bayesian differential privacy preserves three nice properties of differential privacy: sequential composability, parallel composability, and post-processing. In this paper, we provide an affirmative answer to this question; i.e., Bayesian differential privacy still have these properties. The idea behind sequential composability is that if we have $m$ algorithms $Y_1, Y_2, \ldots, Y_m$, where $Y_{\ell}$ is independently $ε_{\ell}$-Bayesian differential private for ${\ell}=1,2,\ldots,m$, then by feeding the result of $Y_1$ into $Y_2$, the result of $Y_2$ into $Y_3$, and so on, we will finally have an $\sum_{\ell=1}^m ε_{\ell}$-Bayesian differential private algorithm. For parallel composability, we consider the situation where a database is partitioned into $m$ disjoint subsets. The $\ell$-th subset is input to a Bayesian differential private algorithm $Y_{\ell}$, for ${\ell}=1,2,\ldots,m$. Then the parallel composition of $Y_1$, $Y_2$, $\ldots$, $Y_m$ will be $\max_{\ell=1}^m ε_{\ell}$-Bayesian differential private. The post-processing property means that a data analyst, without additional knowledge about the private database, cannot compute a function of the output of a Bayesian differential private algorithm and reduce its privacy guarantee.

cs.CR↗

arXiv2019

On Connectivity and Robustness in Random Intersection Graphs

Random intersection graphs have received much attention recently and been used in a wide range of applications ranging from key predistribution in wireless sensor networks to modeling social networks. For these graphs, each node is equipped with a set of objects in a random manner, and two nodes have an undirected edge in between if they have at least one object in common. In this paper, we investigate connectivity and robustness in a general random intersection graph model. Specifically, we establish sharp asymptotic zero-one laws for k-connectivity and k-robustness, as well as the asymptotically exact probability of k-connectivity, for any positive integer k. The k-connectivity property quantifies how resilient is the connectivity of a graph against node or edge failures, while k-robustness measures the effectiveness of local-information-based consensus algorithms (that do not use global graph topology information) in the presence of adversarial nodes. In addition to presenting the results under the general random intersection graph model, we consider two special cases of the general model, a binomial random intersection graph and a uniform random intersection graph, which both have numerous applications as well. For these two specialized graphs, our results on asymptotically exact probabilities of k-connectivity and asymptotic zero-one laws for k-robustness are also novel in the literature.

cs.DM↗

arXiv2019

Analyzing connectivity of heterogeneous secure sensor networks

We analyze connectivity of a heterogeneous secure sensor network that uses key predistribution to protect communications between sensors. For this network on a set $\mathcal{V}_n$ of $n$ sensors, suppose there is a pool $\mathcal{P}_n$ consisting of $P_n$ distinct keys. The $n$ sensors in $\mathcal{V}_n$ are divided into $m$ groups $\mathcal{A}_1, \mathcal{A}_2, \ldots, \mathcal{A}_m$. Each sensor $v$ is independently assigned to exactly a group according to the probability distribution with $\mathbb{P}[v \in \mathcal{A}_i]= a_i$ for $i=1,2,\ldots,m$, where $\sum_{i=1}^{m}a_i = 1$. Afterwards, each sensor in group $\mathcal{A}_i$ independently chooses $K_{i,n}$ keys uniformly at random from the key pool $\mathcal{P}_n$, where $K_{1,n} \leq K_{2,n}\leq \ldots \leq K_{m,n}$. Finally, any two sensors in $\mathcal{V}_n$ establish a secure link in between if and only if they have at least one key in common. We present critical conditions for connectivity of this heterogeneous secure sensor network. The result provides useful guidelines for the design of secure sensor networks. This paper improves the seminal work [1] (IEEE Transactions on Information Theory 2016) of Ya{ğ}an on connectivity in several aspects (omitted due to arxiv abstract's character limitation; see the paper for details).

cs.DM↗

arXiv2019

On secure communication in sensor networks under q-composite key predistribution with unreliable links

Many applications of wireless sensor networks (WSNs) require deploying sensors in hostile environments, where an adversary may eavesdrop communications. To secure communications in WSNs, the q-composite key predistribution scheme has been proposed in the literature. In this paper, we investigate secure k-connectivity in WSNs operating under the q-composite scheme, in consideration of the unreliability of wireless links. Secure k-connectivity ensures that any two sensors can find a path in between for secure communication, even when k - 1 sensors fail. We present conditions on how to set the network parameters such that the network has secure k-connectivity asymptotically almost surely. The result is given in the form of a sharp zero-one law.

cs.IT↗

arXiv2019

On resilience and connectivity of secure wireless sensor networks under node capture attacks

Despite much research on probabilistic key predistribution schemes for wireless sensor networks over the past decade, few formal analyses exist that define schemes' resilience to node-capture attacks precisely and under realistic conditions. In this paper, we analyze the resilience of the q-composite key predistribution scheme, which mitigates the node capture vulnerability of the Eschenauer-Gligor scheme in the neighbor discovery phase. We derive scheme parameters to have a desired level of resiliency, and obtain optimal parameters that defend against different adversaries as much as possible. We also show that this scheme can be easily enhanced to achieve the same "perfect resilience" property as in the random pairwise key predistribution for attacks launched after neighbor discovery. Despite considerable attention to this scheme, much prior work explicitly or implicitly uses an incorrect computation for the probability of link compromise under node-capture attacks and ignores real-world transmission constraints of sensor nodes. Moreover, we derive the critical network parameters to ensure connectivity in both the absence and presence of node-capture attacks. We also investigate node replication attacks by analyzing the adversary's optimal strategy.

cs.CR↗

arXiv2019

k-Connectivity in Random Key Graphs with Unreliable Links

Random key graphs form a class of random intersection graphs and are naturally induced by the random key predistribution scheme of Eschenauer and Gligor for securing wireless sensor network (WSN) communications. Random key graphs have received much interest recently, owing in part to their wide applicability in various domains including recommender systems, social networks, secure sensor networks, clustering and classification analysis, and cryptanalysis to name a few. In this paper, we study connectivity properties of random key graphs in the presence of unreliable links. Unreliability of the edges are captured by independent Bernoulli random variables, rendering edges of the graph to be on or off independently from each other. The resulting model is an intersection of a random key graph and an Erdos-Renyi graph, and is expected to be useful in capturing various real-world networks; e.g., with secure WSN applications in mind, link unreliability can be attributed to harsh environmental conditions severely impairing transmissions. We present conditions on how to scale this model's parameters so that i) the minimum node degree in the graph is at least k, and ii) the graph is k-connected, both with high probability as the number of nodes becomes large. The results are given in the form of zeroone laws with critical thresholds identified and shown to coincide for both graph properties. These findings improve the previous results by Rybarczyk on the k-connectivity of random key graphs (with reliable links), as well as the zero-one laws by Yagan on the 1-connectivity of random key graphs with unreliable links.

cs.IT↗

arXiv2019

Probabilistic key predistribution in mobile networks resilient to node-capture attacks

We present a comprehensive analysis on connectivity and resilience of secure sensor networks under the widely studied q-composite key predistribution scheme. For network connectivity which ensures that any two sensors can find a path in between for secure communication, we derive the conditions to guarantee connectivity in consideration of (i) node-capture attacks, where the adversary may capture a set of sensors and compromise keys in their memory; (ii) sensor mobility, meaning that sensors can move around so that the network topology may change over time; (iii) physical transmission constraints, under which two sensors have to be within each other's transmission range for communication; (iv) the boundary effect of network fields; and (v) link unreliability, meaning that links are allowed to be unreliable. In contrast, many prior connectivity analyses of secure sensor networks often ignore the above issues. For resilience, although limited studies have presented formal analysis, it is often assumed that the adversary captures a random set of sensors, whereas this paper allows the adversary to capture an arbitrary set of sensors. We present conditions to ensure unassailability and unsplittability in secure sensor networks under the q-composite scheme. Unassailability ensures that an adversary capturing any set consisting of a negligible fraction of sensors can compromise only a negligible fraction of communication links although the adversary may compromise communications between noncaptured nodes which happen to use keys that are shared by captured nodes. Unsplittability means that when a negligible fraction of sensors are captured, almost all of the remaining nodes are still securely connected. Based on the results of connectivity, unassailability and unsplittability, we provide useful guidelines for the design of secure sensor networks.

cs.IT↗

arXiv2019

Topological properties of secure wireless sensor networks under the q-composite key predistribution scheme with unreliable links

Security is an important issue in wireless sensor networks (WSNs), which are often deployed in hostile environments. The q-composite key predistribution scheme has been recognized as a suitable approach to secure WSNs. Although the q-composite scheme has received much attention in the literature, there is still a lack of rigorous analysis for secure WSNs operating under the q-composite scheme in consideration of the unreliability of links. One main difficulty lies in analyzing the network topology, whose links are not independent. Wireless links can be unreliable in practice due to the presence of physical barriers between sensors or because of harsh environmental conditions severely impairing communications. In this paper, we resolve the difficult challenge and investigate topological properties related to node degree in WSNs operating under the q-composite scheme with unreliable communication links modeled as independent ON/OFF channels. Specifically, we derive the asymptotically exact probability for the property of minimum degree being at least k, present the asymptotic probability distribution for the minimum degree, and demonstrate that the number of nodes with a fixed degree is in distribution asymptotically equivalent to a Poisson random variable. We further use the theoretical results to provide useful design guidelines for secure WSNs. Experimental results also confirm the validity of our analytical findings.

cs.NI↗

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↗