No abstract uploaded!

No abstract uploaded!

No abstract uploaded!

In this paper, we propose a new algorithm for solving the general approximate common divisors (GACD) problems, which is based on lattice reduction algorithms on certain special lattices and linear equation solving algorithms over integers. Through both theoretical arguments and experimental data, we show that our new algorithm works in polynomial time but under roughly the following condition: \begin{itemize} \item There is a positive integer t such that \frac{\gamma+\eta}{t} + \frac{t}{H} + \rho < \eta; \item We have more than t GACD samples. \end{itemize} or equivalently \begin{itemize} \item H(\eta-\rho)^2 - 4(\gamma+\eta) > 0; \item We have more than t = \lceil\frac{H(\eta-\rho) - \sqrt{H^2(\eta-\rho)^2 - 4H(\gamma+\eta)}{2}\rceil GACD samples. \end{itemize} Here \gamma, \eta and \rho are parameters describing a GACD problem, H = 1/\log_2 F and F is the Hermite Factor. In our experiments, H is shown to be roughly 40 when using the LLL reduction algorithm and it should be around 80 when using Deep or BKZ algorithms. % We use our algorithm to solve concrete problems that no other algorithm could solve before. We show how our algorithm can be applied to attack the fully homomorphic encryption schemes which are based on the general approximate common divisors problem and its limitations.

With the recent rise of Metaverse, online multiplayer VR applications are becoming increasingly prevalent worldwide. Allowing users to move easily in virtual environments is crucial for high-quality experiences in such collaborative VR applications. This paper focuses on redirected walking technology (RDW) to allow users to move beyond the confines of the limited physical environments (PE). The existing RDW methods lack the scheme to coordinate multiple users in different PEs, and thus have the issue of triggering too many resets for all the users. We propose a novel multi-user RDW method that is able to significantly reduce the overall reset number and give users a better immersive experience by providing a more continuous exploration. Our key idea is to first find out the ”bottleneck” user that may cause all users to be reset and estimate the time to reset, and then redirect all the users to favorable poses during that maximized bottleneck time to ensure the subsequent resets can be postponed as much as possible. More particularly, we develop methods to estimate the time of possibly encountering obstacles and the reachable area for a specific pose to enable the prediction of the next reset caused by any user. Our experiments and user study found that our method outperforms existing RDW methods in online VR applications.