arXiv subjects

T. -H. Hubert Chan

Publications and source records attributed to T. -H. Hubert Chan.

4 recordsLinked to original sources

arXiv2025

Indifferential Privacy: A New Paradigm and Its Applications to Optimal Matching in Dark Pool Auctions

Public exchanges like the New York Stock Exchange and NASDAQ act as auctioneers in a public double auction system, where buyers submit their highest bids and sellers offer their lowest asking prices, along with the number of shares (volume) they wish to trade. The auctioneer matches compatible orders and executes the trades when a match is found. However, auctioneers involved in high-volume exchanges, such as dark pools, may not always be reliable. They could exploit their position by engaging in practices like front-running or face significant conflicts of interest, i.e., ethical breaches that have frequently resulted in hefty fines and regulatory scrutiny within the financial industry. Previous solutions, based on the use of fully homomorphic encryption (Asharov et al., AAMAS 2020), encrypt orders ensuring that information is revealed only when a match occurs. However, this approach introduces significant computational overhead, making it impractical for high-frequency trading environments such as dark pools. In this work, we propose a new system based on differential privacy combined with lightweight encryption, offering an efficient and practical solution that mitigates the risks of an untrustworthy auctioneer. Specifically, we introduce a new concept called Indifferential Privacy, which can be of independent interest, where a user is indifferent to whether certain information is revealed after some special event, unlike standard differential privacy. For example, in an auction, it's reasonable to disclose the true volume of a trade once all of it has been matched. Moreover, our new concept of Indifferential Privacy allows for maximum matching, which is impossible with conventional differential privacy.

cs.CR↗

arXiv2022

A Feedforward Unitary Equivariant Neural Network

We devise a new type of feedforward neural network. It is equivariant with respect to the unitary group $U(n)$. The input and output can be vectors in $\mathbb{C}^n$ with arbitrary dimension $n$. No convolution layer is required in our implementation. We avoid errors due to truncated higher order terms in Fourier-like transformation. The implementation of each layer can be done efficiently using simple calculations. As a proof of concept, we have given empirical results on the prediction of the dynamics of atomic motion to demonstrate the practicality of our approach.

cs.LG↗

arXiv2020

Efficient and DoS-resistant Consensus for Permissioned Blockchains

Existing permissioned blockchain systems designate a fixed and explicit group of committee nodes to run a consensus protocol that confirms the same sequence of blocks among all nodes. Unfortunately, when such a permissioned blockchain runs in a large scale on the Internet, these explicit committee nodes can be easily turned down by denial-of-service (DoS) or network partition attacks. Although work proposes scalable BFT protocols that run on a larger number of committee nodes, their efficiency drops dramatically when only a small number of nodes are attacked. In this paper, our EGES protocol leverages Intel SGX to develop a new abstraction called "stealth committee", which effectively hides the committee nodes into a large pool of fake committee nodes. EGES selects a distinct group of stealth committee for each block and confirms the same sequence of blocks among all nodes with overwhelming probability. Evaluation on typical geo-distributed settings shows that: (1)EGES is the first permissioned blockchain's consensus protocol that can tolerate tough DoS and network partition attacks; and (2) EGES achieves comparable throughput and latency as existing permissioned blockchains' protocols

cs.DC↗

arXiv2015

Influence Maximization under The Non-progressive Linear Threshold Model

In the problem of influence maximization in information networks, the objective is to choose a set of initially active nodes subject to some budget constraints such that the expected number of active nodes over time is maximized. The linear threshold model has been introduced to study the opinion cascading behavior, for instance, the spread of products and innovations. In this paper, we we extends the classic linear threshold model [18] to capture the non-progressive be- havior. The information maximization problem under our model is proved to be NP-Hard, even for the case when the underlying network has no directed cycles. The first result of this paper is negative. In general, the objective function of the extended linear threshold model is no longer submodular, and hence the hill climbing approach that is commonly used in the existing studies is not applicable. Next, as the main result of this paper, we prove that if the underlying information network is directed acyclic, the objective function is submodular (and monotone). Therefore, in directed acyclic networks with a specified budget we can achieve 1/2 -approximation on maximizing the number of active nodes over a certain period of time by a deterministic algorithm, and achieve the (1 - 1/e )-approximation by a randomized algorithm.

cs.SI↗