Qiang Tang

Yuejun Guo, Seifeddine Bettaieb, Qiang Hu et al.

Representing source code in a generic input format is crucial to automate software engineering tasks, e.g., applying machine learning algorithms to extract information. Visualizing code representations can further enable human experts to gain an intuitive insight into the code. Unfortunately, as of today, there is no universal tool that can simultaneously visualise different types of code representations. In this paper, we introduce a tool, CodeLens, which provides a visual interaction environment that supports various representation methods and helps developers understand and explore them. CodeLens is designed to support multiple programming languages, such as Java, Python, and JavaScript, and four types of code representations, including sequence of tokens, abstract syntax tree (AST), data flow graph (DFG), and control flow graph (CFG). By using CodeLens, developers can quickly visualize the specific code representation and also obtain the represented inputs for models of code. The Web-based interface of CodeLens is available at http://www.codelens.org. The demonstration video can be found at http://www.codelens.org/demo.

Zanis Ali Khan, Aayush Garg, Qiang Tang

Software vulnerabilities pose significant security threats, requiring effective mitigation. While Automated Program Repair (APR) has advanced in fixing general bugs, vulnerability patching, a security-critical aspect of APR remains underexplored. This study investigates pre-trained language models, CodeBERT and CodeT5, for automated vulnerability patching across six datasets and four languages. We evaluate their accuracy and generalization to unknown vulnerabilities. Results show that while both models face challenges with fragmented or sparse context, CodeBERT performs comparatively better in such scenarios, whereas CodeT5 excels in capturing complex vulnerability patterns. CodeT5 also demonstrates superior scalability. Furthermore, we test fine-tuned models on both in-distribution (trained) and out-of-distribution (unseen) datasets. While fine-tuning improves in-distribution performance, models struggle to generalize to unseen data, highlighting challenges in robust vulnerability detection. This study benchmarks model performance, identifies limitations in generalization, and provides actionable insights to advance automated vulnerability patching for real-world security applications.

Yingzi Gao, Yuan Lu, Zhenliang Lu et al.

Efficient asynchronous Byzantine agreement (BA) protocols were mostly studied with private setups, e.g., pre-setup threshold cryptosystem. Challenges remain to reduce the large communication in the absence of such setups. Recently, Abraham et al. (PODC'21) presented the first asynchronous validated BA (VBA) with expected $O(n^3)$ messages and $O(1)$ rounds, relying on only public key infrastructure (PKI) setup, but the design still costs $O(λn^3 \log n)$ bits. Here $n$ is the number of parties, and $λ$ is a cryptographic security parameter. In this paper, we reduce the communication of private-setup free asynchronous BA to expected $O(λn^3)$ bits. At the core of our design, we give a systematic treatment of common randomness protocols in the asynchronous network, and proceed as: - We give an efficient reasonably fair common coin protocol in the asynchronous setting with only PKI setup. It costs only $O(λn^3)$ bits and $O(1)$ rounds, and ensures that with at least 1/3 probability, all honest parties can output a common bit that is as if randomly flipped. This directly renders more efficient private-setup free asynchronous binary agreement (ABA) with expected $O(λn^3)$ bits and $O(1)$ rounds. - Then, we lift our common coin to attain perfect agreement by using a single ABA. This gives us a reasonably fair random leader election protocol with expected $O(λn^3)$ communication and expected constant rounds. It is pluggable in all existing VBA protocols (e.g., Cachin et al., CRYPTO'01; Abraham et al., PODC'19; Lu et al., PODC'20) to remove the needed private setup or distributed key generation (DKG). As such, the communication of private-setup free VBA is reduced to expected $O(λn^3)$ bits while preserving fast termination in expected $O(1)$ rounds.

Yuan Lu, Zhenliang Lu, Qiang Tang

An urgent demand of deploying BFT consensus over the Internet is raised for implementing blockchain services. The deterministic (partial) synchronous protocols can be simple and fast in good network conditions, but are subject to denial-of-service when synchrony assumption fails. Asynchronous protocols, on the contrary, are robust against the adversarial network, but are substantially more complicated and slower for the inherent use of randomness. Facing the issues, optimistic asynchronous atomic broadcast ( Kursawe-Shoup, 2002; Ramasamy-Cachin, 2005) was proposed to improve the normal-case performance of the slow asynchronous consensus. They run a deterministic fastlane if the network condition remains good, and can fall back to a fully asynchronous protocol via a pace-synchronization mechanism if the fastlane fails. Unfortunately, existing pace-synchronization directly uses a heavy tool of asynchronous multi-valued validated Byzantine agreement (MVBA). We present Bolt-Dumbo Transformer (BDT), a generic framework for practical optimistic asynchronous atomic broadcast. At the core of BDT, we set forth a new fastlane abstraction that is simple and fast, while preparing honest parties to gracefully face potential fastlane failures caused by malicious leader or bad network. This enables a highly efficient pace-synchronization to handle fallback. The resulting design reduces a cumbersome MVBA to a variant of the conceptually simplest binary agreement only. Besides detailed security analyses, we also give concrete instantiations of our framework and implement them. Extensive experiments demonstrate that BDT can enjoy both the low latency of deterministic protocols (e.g. 2-chain version of HotStuff) and the robustness of state-of-the-art asynchronous protocols in practice.

Songlin He, Yuan Lu, Qiang Tang et al.

Peer-to-peer (p2p) content delivery is promising to provide benefits like cost-saving and scalable peak-demand handling in comparison with conventional content delivery networks (CDNs) and complement the decentralized storage networks such as Filecoin. However, reliable p2p delivery requires proper enforcement of delivery fairness, i.e., the deliverers should be rewarded according to their in-time delivery. Unfortunately, most existing studies on delivery fairness are based on non-cooperative game-theoretic assumptions that are arguably unrealistic in the ad-hoc p2p setting. We for the first time put forth the expressive yet still minimalist securities for p2p content delivery, and give two efficient solutions FairDownload and FairStream via the blockchain for p2p downloading and p2p streaming scenarios, respectively. Our designs not only guarantee delivery fairness to ensure deliverers be paid (nearly) proportional to his in-time delivery, but also ensure the content consumers and content providers to be fairly treated. The fairness of each party can be guaranteed when the other two parties collude to arbitrarily misbehave. Moreover, the systems are efficient in the sense of attaining asymptotically optimal on-chain costs and optimal deliverer communication. We implement the protocols to build the prototype systems atop the Ethereum Ropsten network. Extensive experiments done in LAN and WAN settings showcase their high practicality.

Qiang Tang

The COVID-19 pandemic has posed a unique challenge for the world to find solutions, ranging from vaccines to ICT solutions to slow down the virus spreading. Due to the highly contagious nature of the virus, social distancing is one fundamental measure which has already adopted by many countries. At the technical level, this prioritises contact tracing solutions, which can alert the users who have been in close contact with the infected persons and meanwhile allow heath authorities to take proper actions. In this paper, we examine several existing privacy-aware contact tracing solutions and analyse their (dis)advantages. At the end, we describe several major observations and outline an interdisciplinary research agenda towards more comprehensive and effective privacy-aware contact tracing solutions.

Yuan Lu, Qiang Tang, Guiling Wang

With the rapid popularity of blockchain, decentralized human intelligence tasks (HITs) are proposed to crowdsource human knowledge without relying on vulnerable third-party platforms. However, the inherent limits of blockchain cause decentralized HITs to face a few "new" challenges. For example, the confidentiality of solicited data turns out to be the sine qua non, though it was an arguably dispensable property in the centralized setting. To ensure the "new" requirement of data privacy, existing decentralized HITs use generic zero-knowledge proof frameworks (e.g. SNARK), but scarcely perform well in practice, due to the inherently expensive cost of generality. We present a practical decentralized protocol for HITs, which also achieves the fairness between requesters and workers. At the core of our contributions, we avoid the powerful yet highly-costly generic zk-proof tools and propose a special-purpose scheme to prove the quality of encrypted data. By various non-trivial statement reformations, proving the quality of encrypted data is reduced to efficient verifiable decryption, thus making decentralized HITs practical. Along the way, we rigorously define the ideal functionality of decentralized HITs and then prove the security due to the ideal-real paradigm. We further instantiate our protocol to implement a system called Dragoon, an instance of which is deployed atop Ethereum to facilitate an image annotation task used by ImageNet. Our evaluations demonstrate its practicality: the on-chain handling cost of Dragoon is even less than the handling fee of Amazon's Mechanical Turk for the same ImageNet HIT.

Qiang Tang

Collaborative filtering recommenders provide effective personalization services at the cost of sacrificing the privacy of their end users. Due to the increasing concerns from the society and stricter privacy regulations, it is an urgent research challenge to design privacy-preserving and yet robust recommenders which offer recommendation services to privacy-aware users. Our analysis shows that existing solutions fall short in several aspects, including lacking attention to the precise output to end users and ignoring the correlated robustness issues. In this paper, we provide a general system structure for latent factor based collaborative filtering recommenders by formulating them into model training and prediction computing stages, and also describe a new security model. Aiming at pragmatic solutions, we first show how to construct privacy-preserving and yet robust model training stage based on existing solutions. Then, we propose two cryptographic protocols to realize a privacy-preserving prediction computing stage, depending on whether or not an extra proxy is involved. Different from standard Top-k recommendations, we alternatively let the end user retrieve the unrated items whose predictions are above a threshold, as a result of our privacy by design strategy. Experimental results show that our new protocols are quite efficient.

Qiang Tang

Distributed Leger Technologies (DLTs), most notably Blockchain technologies, bring decentralised platforms that eliminate a single trusted third party and avoid the notorious single point of failure vulnerability. Since Nakamoto's Bitcoin cryptocurrency system, an enormous number of decentralised applications have been proposed on top of these technologies, aiming at more transparency and trustworthiness than their traditional counterparts. These applications spread over a lot of areas, e.g. financial services, healthcare, transportation, supply chain management, and cloud computing. While Blockchain brings transparency and decentralised trust intuitively due to the consensus of a (very large) group of nodes (or, miners), it introduces very subtle implications for other desirable properties such as privacy. In this work, we demonstrate these subtle implications for Blockchain-based searchable encryption solutions, which are one specific use case of cloud computing services. These solutions rely on Blockchain to achieve both the standard privacy property and the new fairness property, which requires that search operations are carried out faithfully and are rewarded accordingly. We show that directly replacing the server in an existing searchable encryption solution with a Blockchain will cause undesirable operational cost, privacy loss, and security vulnerabilities. The analysis results indicate that a dedicated server is still needed to achieve the desired privacy guarantee. To this end, we propose two frameworks which can be instantiated based on most existing searchable encryption schemes. Through analysing these two frameworks, we affirmatively show that a carefully engineered Blockchain-based solution can achieve the desired fairness property while preserving the privacy guarantee of the original searchable encryption scheme simultaneously.

Yuan Lu, Qiang Tang, Guiling Wang

We design and implement the first private and anonymous decentralized crowdsourcing system ZebraLancer, and overcome two fundamental challenges of decentralizing crowdsourcing, i.e., data leakage and identity breach. First, our outsource-then-prove methodology resolves the tension between the blockchain transparency and the data confidentiality to guarantee the basic utilities/fairness requirements of data crowdsourcing, thus ensuring: (i) a requester will not pay more than what data deserve, according to a policy announced when her task is published via the blockchain; (ii) each worker indeed gets a payment based on the policy, if he submits data to the blockchain; (iii) the above properties are realized not only without a central arbiter, but also without leaking the data to the open blockchain. Second, the transparency of blockchain allows one to infer private information about workers and requesters through their participation history. Simply enabling anonymity is seemingly attempting but will allow malicious workers to submit multiple times to reap rewards. ZebraLancer also overcomes this problem by allowing anonymous requests/submissions without sacrificing accountability. The idea behind is a subtle linkability: if a worker submits twice to a task, anyone can link the submissions, or else he stays anonymous and unlinkable across tasks. To realize this delicate linkability, we put forward a novel cryptographic concept, i.e., the common-prefix-linkable anonymous authentication. We remark the new anonymous authentication scheme might be of independent interest. Finally, we implement our protocol for a common image annotation task and deploy it in a test net of Ethereum. The experiment results show the applicability of our protocol atop the existing real-world blockchain.

Jun Wang, Afonso Arriaga, Qiang Tang et al.

Recommender systems rely on large datasets of historical data and entail serious privacy risks. A server offering Recommendation as a Service to a client might leak more information than necessary regarding its recommendation model and dataset. At the same time, the disclosure of the client's preferences to the server is also a matter of concern. Devising privacy-preserving protocols using general cryptographic primitives (e.g., secure multi-party computation or homomorphic encryption), is a typical approach to overcome privacy concerns, but in conjunction with state-of-the-art recommender systems often yields far-from-practical solutions. In this paper, we tackle this problem from the direction of constructing crypto-friendly machine learning algorithms. In particular, we propose CryptoRec, a secure two-party computation protocol for Recommendation as a Service, which encompasses a novel recommender system. This model possesses two interesting properties: (1) It models user-item interactions in an item-only latent feature space in which personalized user representations are automatically captured by an aggregation of pre-learned item features. This means that a server with a pre-trained model can provide recommendations for a client whose data is not in its training set. Nevertheless, re-training the model with the client's data still improves accuracy. (2) It only uses addition and multiplication operations, making the model straightforwardly compatible with homomorphic encryption schemes. We demonstrate the efficiency and accuracy of CryptoRec on three real-world datasets. CryptoRec allows a server with thousands of items to privately answer a prediction query within a few seconds on a single PC, while its prediction accuracy is still competitive with state-of-the-art recommender systems computing over clear data.

Balazs Pejo, Qiang Tang, Gergely Biczok

Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have the necessary data to train a reasonably accurate model. For such organizations, a realistic solution is to train their machine learning models based on their joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, by focusing on a two-player setting, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player. Using recommendation systems as our main use case, we demonstrate how two players can make practical use of the proposed theoretical framework, including setting up the parameters and approximating the non-trivial Nash Equilibrium.

Jun Wang, Qiang Tang

Privacy issues of recommender systems have become a hot topic for the society as such systems are appearing in every corner of our life. In contrast to the fact that many secure multi-party computation protocols have been proposed to prevent information leakage in the process of recommendation computation, very little has been done to restrict the information leakage from the recommendation results. In this paper, we apply the differential privacy concept to neighborhood-based recommendation methods (NBMs) under a probabilistic framework. We first present a solution, by directly calibrating Laplace noise into the training process, to differential-privately find the maximum a posteriori parameters similarity. Then we connect differential privacy to NBMs by exploiting a recent observation that sampling from the scaled posterior distribution of a Bayesian model results in provably differentially private systems. Our experiments show that both solutions allow promising accuracy with a modest privacy budget, and the second solution yields better accuracy if the sampling asymptotically converges. We also compare our solutions to the recent differentially private matrix factorization (MF) recommender systems, and show that our solutions achieve better accuracy when the privacy budget is reasonably small. This is an interesting result because MF systems often offer better accuracy when differential privacy is not applied.

Jun Wang, Qiang Tang

Probabilistic graphic model is an elegant framework to compactly present complex real-world observations by modeling uncertainty and logical flow (conditionally independent factors). In this paper, we present a probabilistic framework of neighborhood-based recommendation methods (PNBM) in which similarity is regarded as an unobserved factor. Thus, PNBM leads the estimation of user preference to maximizing a posterior over similarity. We further introduce a novel multi-layer similarity descriptor which models and learns the joint influence of various features under PNBM, and name the new framework MPNBM. Empirical results on real-world datasets show that MPNBM allows very accurate estimation of user preferences.