arXiv subjects

Zhiyi Zhang

Publications and source records attributed to Zhiyi Zhang.

35 records · Page 2Linked to original sources

arXiv2022

A Normal Test for Independence via Generalized Mutual Information

Testing hypothesis of independence between two random elements on a joint alphabet is a fundamental exercise in statistics. Pearson's chi-squared test is an effective test for such a situation when the contingency table is relatively small. General statistical tools are lacking when the contingency data tables are large or sparse. A test based on generalized mutual information is derived and proposed in this article. The new test has two desired theoretical properties. First, the test statistic is asymptotically normal under the hypothesis of independence; consequently it does not require the knowledge of the row and column sizes of the contingency table. Second, the test is consistent and therefore it would detect any form of dependence structure in the general alternative space given a sufficiently large sample. In addition, simulation studies show that the proposed test converges faster than Pearson's chi-squared test when the contingency table is large or sparse.

math.ST↗

arXiv2022

Interpreting Class Conditional GANs with Channel Awareness

Understanding the mechanism of generative adversarial networks (GANs) helps us better use GANs for downstream applications. Existing efforts mainly target interpreting unconditional models, leaving it less explored how a conditional GAN learns to render images regarding various categories. This work fills in this gap by investigating how a class conditional generator unifies the synthesis of multiple classes. For this purpose, we dive into the widely used class-conditional batch normalization (CCBN), and observe that each feature channel is activated at varying degrees given different categorical embeddings. To describe such a phenomenon, we propose channel awareness, which quantitatively characterizes how a single channel contributes to the final synthesis. Extensive evaluations and analyses on the BigGAN model pre-trained on ImageNet reveal that only a subset of channels is primarily responsible for the generation of a particular category, similar categories (e.g., cat and dog) usually get related to some same channels, and some channels turn out to share information across all classes. For good measure, our algorithm enables several novel applications with conditional GANs. Concretely, we achieve (1) versatile image editing via simply altering a single channel and manage to (2) harmoniously hybridize two different classes. We further verify that the proposed channel awareness shows promising potential in (3) segmenting the synthesized image and (4) evaluating the category-wise synthesis performance.

cs.CV↗

arXiv2021

One-Shot Generative Domain Adaptation

This work aims at transferring a Generative Adversarial Network (GAN) pre-trained on one image domain to a new domain referring to as few as just one target image. The main challenge is that, under limited supervision, it is extremely difficult to synthesize photo-realistic and highly diverse images, while acquiring representative characters of the target. Different from existing approaches that adopt the vanilla fine-tuning strategy, we import two lightweight modules to the generator and the discriminator respectively. Concretely, we introduce an attribute adaptor into the generator yet freeze its original parameters, through which it can reuse the prior knowledge to the most extent and hence maintain the synthesis quality and diversity. We then equip the well-learned discriminator backbone with an attribute classifier to ensure that the generator captures the appropriate characters from the reference. Furthermore, considering the poor diversity of the training data (i.e., as few as only one image), we propose to also constrain the diversity of the generative domain in the training process, alleviating the optimization difficulty. Our approach brings appealing results under various settings, substantially surpassing state-of-the-art alternatives, especially in terms of synthesis diversity. Noticeably, our method works well even with large domain gaps, and robustly converges within a few minutes for each experiment.

cs.CV↗

arXiv2021

Sovereign: User-Controlled Smart Homes

Recent years have witnessed the rapid deployment of smart homes; most of them are controlled by remote servers in the cloud. Such designs raise security and privacy concerns for end users. In this paper, we describe the design of Sovereign, a home IoT system framework that provides end users complete control of their home IoT systems. Sovereign lets home IoT devices and applications communicate via application-named data and secures data directly. This enables direct, secure, one-to-one and one-to-many device-to-device communication over wireless broadcast media. Sovereign utilizes semantic names to construct usable security solutions. We implement Sovereign as a publish-subscribe-based development platform together with a prototype home IoT controller. Our preliminary evaluation shows that Sovereign provides a systematic, easy-to-use solution to user-controlled, self-contained smart homes running on existing IoT hardware without imposing noticeable overhead.

cs.NI↗

arXiv2021

Supporting Multiparty Signing over Named Data Networking

Modern digitally controlled systems require multiparty authentication and authorization to meet the desired security requirement. This paper describes the design and development of NDN-MPS, an automated solution to support multiparty signature signing and verification for NDN-enabled applications. NDN-MPS suggests several changes and extensions to the existing NDN security solutions. First, it introduces a new type of trust schema to support signing and verification for multiple signers under complex policies such as threshold schemes. Second, it extends the NDN signature format to accommodate multisignature schemes such as BLS signature. Third, it introduces a signature collection protocol to solicit signatures securely from multiple signers. We further evaluate NDN-MPS by assessing its security properties and measuring its performance.

cs.CR↗

arXiv2021

Expect More from the Networking: DDoS Mitigation by FITT in Named Data Networking

Distributed Denial of Service (DDoS) attacks have plagued the Internet for decades, but the basic defense approaches have not fundamentally changed. Rather, the size and rate of growth in attacks have actually outpaced carriers' and DDoS mitigation services' growth, calling for new solutions that can be, partially or fully, deployed imminently and exhibit effectiveness. In this paper, we examine the basic functions in Named Data Networking (NDN), a newly proposed Internet architecture, that can address the principle weaknesses in today's IP networks. We demonstrate by a new DDoS mitigation solution over NDN, Fine-grained Interest Traffic Throttling FITT, that NDN's architectural changes, even when incrementally deployed, can make DDoS attacks fundamentally more difficult to launch and less effective. FITT leverages the NDN design to enable the network to detect DDoS from victim's feedback, throttles DDoS traffic by reverse its exact paths through the network, and enforces control over the misbehaving entities at their sources. Our extensive simulation results show that FITT can throttle attack traffic with one-way time delay from the victim to the NDN gateway; upon activation, FITT effectively stop attack traffic from impacting benign flows, resulting in over 99\% of packets reaching victims being legitimate ones. We further demonstrate that service providers may implement NDN/FITT on existing CDN nodes as an incrementally deployable solution to effectuate the application level remediation at the sources, which remains unattainable in today's DDoS mitigation approaches.

cs.CR↗

arXiv2020

On Certificate Management in Named Data Networking

Named Data Networking (NDN) secures network communications by requiring all data packets to be signed when produced. This requirement necessitates efficient and usable mechanisms to handle NDN certificate issuance and revocation, making these supporting mechanisms essential for NDN operations. In this paper, we first investigate and clarify core concepts related to NDN certificates and security design in general, and then present the model of NDN certificate management and its desired properties. We proceed with the design of a specific realization of NDN's certificate management, NDNCERT, evaluate it using a formal security analysis, and discuss the challenges in designing, implementing, and deploying the system, to share our experiences with other NDN security protocol development efforts.

cs.CR↗

arXiv2020

EL PASSO: Privacy-preserving, Asynchronous Single Sign-On

We introduce EL PASSO, a privacy-preserving, asynchronous Single Sign-On (SSO) system. It enables personal authentication while protecting users' privacy against both identity providers and relying parties, and allows selective attribute disclosure. EL PASSO is based on anonymous credentials, yet it supports users' accountability. Selected authorities may recover the identity of allegedly misbehaving users, and users can prove properties about their identity without revealing it in the clear. EL PASSO does not require specific secure hardware or a third party (other than existing participants in SSO). The generation and use of authentication credentials are asynchronous, allowing users to sign on when identity providers are temporarily unavailable. We evaluate EL PASSO in a distributed environment and prove its low computational cost, yielding faster sign-on operations than OIDC from a regular laptop, one-second user-perceived latency from a low-power device, and scaling to more than 50 sign-on operations per second at a relying party using a single 4-core server in the cloud.

cs.CR↗

arXiv2020

AuditShare: Sensitive Data Sharing with Reliable Leaker Identification

As Personally Identifiable Information (PII) data sharing among multiple parties becomes increasingly common, so does the potential for data leakage. As required by new data protection regulations and laws, when PII leakage occurs, one must be able to reliably identify the leaking sources. Existing solutions utilize watermark technologies or data object allocation strategies to differentiate the data shared with different parties to identify potential leakers. However, these solutions lose their effectiveness under several attack scenarios, e.g., a data sender may leak the data and a receiver may deny the reception of certain shared data. Worse yet, multiple receivers might collude and apply a set of operations such as intersection, complement, and union to their received datasets before leaking them, making the task of leaker identification even more difficult. In this paper, we propose AuditShare, a PII dataset sharing system with reliable leaking source identification. Firstly, taking advantage of the intrinsic properties of PII data, AuditShare allocates data objects to individual sharing parties by PII attributes. Secondly, AuditShare obliviously transfers data between the sender and each receiver and uses a Merkle Tree as an immutable record of the sharing. Thirdly, a knowledge-based identification algorithm is proposed to identify a guilty sender or colluding/non-colluding receivers. Through our evaluation, we show that: (i) With a modest amount of leaked data, AuditShare can accurately (accuracy>99.99%) and undeniably identify all the guilty parties in different cases; (ii) It only takes 0.5 second to share 100,000 data objects in AuditShare, which is practical in real-world deployment.

cs.CR↗

arXiv2019

DLedger: An IoT-Friendly Private Distributed Ledger System Based on DAG

With the ever growing Internet of Things (IoT) market, ledger systems are facing new challenges to efficiently store and secure enormous customer records collected by the IoT devices. The authenticity, availability, and integrity of these records are critically important for both business providers and customers. In this paper, we describe DLedger, a lightweight and resilient distributed ledger system. Instead of a single chain of blocks, DLedger builds the ledger over a directed acyclic graph (DAG), so that its operations can tolerate network partition and intermittent connectivity. Instead of compute-intensive Proof-of-Work (PoW), DLedger utilizes Proof-of-Authentication (PoA), whose light-weight operations are IoT-friendly, to achieve consensus. Furthermore, DLedger is built upon a data-centric network called Named Data Networking (NDN), which facilitates the peer-to-peer data dissemination in heterogeneous IoT networks.

cs.CR↗

arXiv2019

Generalized Mutual Information

Mutual information is one of the essential building blocks of information theory. Yet, it is only finitely defined for distributions with fast decaying tails on a countable joint alphabet of two random elements. The unboundedness of mutual information over the general class of all distributions on a joint alphabet prevents its potential utility to be fully realized. This is in fact a void in the foundation of information theory that needs to be filled. This article proposes a family of generalized mutual information all of whose members 1) are finitely defined for each and every distribution of two random elements on a joint countable alphabet, except the one by Shannon, and 2) enjoy all utilities of a finite Shannon's mutual information.

cs.IT↗

arXiv2019

METTLE: a METamorphic testing approach to assessing and validating unsupervised machine LEarning systems

Unsupervised machine learning is the training of an artificial intelligence system using information that is neither classified nor labeled, with a view to modeling the underlying structure or distribution in a dataset. Since unsupervised machine learning systems are widely used in many real-world applications, assessing the appropriateness of these systems and validating their implementations with respect to individual users' requirements and specific application scenarios$\,/\,$contexts are indisputably two important tasks. Such assessment and validation tasks, however, are fairly challenging due to the absence of a priori knowledge of the data. In view of this challenge, we develop a $\textbf{MET}$amorphic $\textbf{T}$esting approach to assessing and validating unsupervised machine $\textbf{LE}$arning systems, abbreviated as METTLE. Our approach provides a new way to unveil the (possibly latent) characteristics of various machine learning systems, by explicitly considering the specific expectations and requirements of these systems from individual users' perspectives. To support METTLE, we have further formulated 11 generic metamorphic relations (MRs), covering users' generally expected characteristics that should be possessed by machine learning systems. To demonstrate the viability and effectiveness of METTLE we have performed an experiment involving six commonly used clustering systems. Our experiment has shown that, guided by user-defined MR-based adequacy criteria, end users are able to assess, validate, and select appropriate clustering systems in accordance with their own specific needs. Our investigation has also yielded insightful understanding and interpretation of the behavior of the machine learning systems from an end-user software engineering's perspective, rather than a designer's or implementor's perspective, who normally adopts a theoretical approach.

cs.SE↗

arXiv2019

NAC: Automating Access Control via Named Data

In this paper we present the design of Name-based Access Control (NAC) scheme, which supports data confidentiality and access control in Named Data Networking (NDN) architecture by encrypting content at the time of production, and by automating the distribution of encryption and decryption keys. NAC achieves the above design goals by leveraging specially crafted NDN naming conventions to define and enforce access control policies, and to automate the cryptographic key management. The paper also explains how NDN's hierarchically structured namespace allows NAC to support fine-grained access control policies, and how NDN's Interest-Data exchange can help NAC to function in case of intermittent connectivity. Moreover, we show that NAC design can be further extended to support Attribute-based Encryption (ABE), which supports access control with additional levels of flexibility and scalability.

cs.NI↗

arXiv2018

Sample Size for Concurrent Species Detection in a Species-Rich Assemblage

Monitoring the distribution of microfossils in stratigraphic successions is an essential tool for biostratigraphic, evolutionary and paleoecologic/paleoceanographic studies. To estimate the relative abundance (%) of a given species, it is necessary to estimate in advance the minimum number of specimens to be used in the count (n). This requires an a priori assumption about a specified level of confidence, and about the species population proportion (p). It is common use to apply the binomial distribution to determine n to detect the presence of more than one species in the same sample, although the multinomial distribution should necessarily be used instead. The mathematical theory of sample size computation using the multinomial distribution is adapted to the computation of n for any number of species to be detected together (K) at any level of confidence. Easy-to-use extensive tables show n, for a combination of K and p. These tables indicate a large difference for n between that indicated by the binomial and those by the multinomial distribution when many species are to be detected simultaneously. Counting only 300 specimens (with 95 % confidence level) or 500 (99 %) is not enough to detect more than one taxon. The reconstructed history of the micro-biosphere may therefore, in many instances, need to be largely revised. This revision should affect our understanding of the ecological and evolutionary relationships between the past changes in the biosphere and the other major reservoirs (hydrosphere, geosphere and atmosphere). In biostratigraphy and biochronology, using a much larger sample size, when more than one marker species is to be detected in the neighborhood of the same biozone boundary, may help clarifying the nature of the apparent inconsistencies given by the observed reversals in the ordinal (rank) biostratigraphic data shown as intersections of the correlation lines

q-bio.QM↗

arXiv2015

Domains of Attraction on Countable Alphabets

For each probability distribution on a countable alphabet, a sequence of positive functionals are developed as tail indices based on Turing's perspective. By and only by the asymptotic behavior of these indices, domains of attraction for all probability distributions on the alphabet are defined. The three main domains of attraction are shown to contain distributions with thick tails, thin tails and no tails respectively, resembling in parallel the three main domains of attraction, Gumbel, Frechet and Weibull families, for continuous random variables on the real line. In addition to the probabilistic merits associated with the domains, the tail indices are partially motivated by the fact that there exists an unbiased estimator for every index in the sequence, which is therefore statistically observable, provided that the sample is sufficiently large.

math.PR↗

arXiv2014

Entropic Representation and Estimation of Diversity Indices

This paper serves a twofold purpose. First, a unified perspective on diversity indices is introduced based on an entropic basis. It is shown that the class of all linear combinations of the entropic basis, referred to as the class of linear diversity indices, covers a wide range of diversity indices used in the literature. Second, a class of estimators for linear diversity indices is proposed and it is shown that these estimators have rapidly decaying biases and asymptotic normality.

math.ST↗

arXiv2009

Asymptotic normality of a nonparametric estimator of sample coverage

This paper establishes a necessary and sufficient condition for the asymptotic normality of the nonparametric estimator of sample coverage proposed by Good [Biometrica 40 (1953) 237--264]. This new necessary and sufficient condition extends the validity of the asymptotic normality beyond the previously proven cases.

math.ST↗