Zhiyi Zhang

Huang Huang, Fei Yu, Jianqing Zhu et al.

This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. Significant concerns emerge when addressing cultural sensitivity and local values. To address this, the paper proposes a comprehensive solution that includes further pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic, alongside Reinforcement Learning with AI Feedback (RLAIF) employing a reward model attuned to local culture and values. The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities. Comprehensive evaluations reveal that the resulting model, dubbed `AceGPT', sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. Codes, data, and models are in https://github.com/FreedomIntelligence/AceGPT.

Zhihong Chen, Feng Jiang, Junying Chen et al.

This paper presents our efforts to democratize ChatGPT across language. We release a large language model "Phoenix", achieving competitive performance among open-source English and Chinese models while excelling in languages with limited resources (covering both Latin and non-Latin languages). We believe this work will be beneficial to make ChatGPT more accessible, especially in countries where people cannot use ChatGPT due to restrictions from OpenAI or local goverments. Our data, code, and models are available at https://github.com/FreedomIntelligence/LLMZoo.

Haowen Wang, Xinyan Ye, Yangze Zhou et al.

Uplift modeling is a fundamental component of marketing effect modeling, which is commonly employed to evaluate the effects of treatments on outcomes. Through uplift modeling, we can identify the treatment with the greatest benefit. On the other side, we can identify clients who are likely to make favorable decisions in response to a certain treatment. In the past, uplift modeling approaches relied heavily on the difference-in-difference (DID) architecture, paired with a machine learning model as the estimation learner, while neglecting the link and confidential information between features. We proposed a framework based on graph neural networks that combine causal knowledge with an estimate of uplift value. Firstly, we presented a causal representation technique based on CATE (conditional average treatment effect) estimation and adjacency matrix structure learning. Secondly, we suggested a more scalable uplift modeling framework based on graph convolution networks for combining causal knowledge. Our findings demonstrate that this method works effectively for predicting uplift values, with small errors in typical simulated data, and its effectiveness has been verified in actual industry marketing data.

Zhiyi Zhang, Siqi Liu, Randy King et al.

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.

Zhiyi Zhang, Su Yong Wong, Junxiao Shi et al.

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.

Zhiyi Zhang, Tianyuan Yu, Xinyu Ma et al.

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.

Zhiyi Zhang, Michał Król, Alberto Sonnino et al.

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.

Zhiyi Zhang, Yu Guan, Xinyu Ma et al.

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.

Zhiyi Zhang, Vishrant Vasavada, Siva Kesava Reddy Kakarla et al.

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.

Zhiyi Zhang, Vishrant Vasavada, Xinyu Ma et al.

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.