Shufan Zhang

Thomas Humphries, Tim Li, Shufan Zhang et al.

It can be difficult for practitioners to interpret the quality of differentially private (DP) statistics due to the added noise. One method to help analysts understand the amount of error introduced by DP is to return a Randomization Interval (RI), along with the statistic. A RI is a type of confidence interval that bounds the error introduced by DP. For queries where the noise distribution depends on the input, such as the median, prior work degrades the quality of the median itself to obtain a high-quality RI. In this work, we propose PostRI, a solution to compute a RI after the median has been estimated. PostRI enables a median estimation with 14%-850% higher utility than related work, while maintaining a narrow RI.

Kuofei Fang, Xinyi Che, Haomin Ouyang et al.

Embodied AI is a prominent research topic in both academia and industry. Current research centers on completing tasks based on explicit user instructions. However, for robots to integrate into human society, they must understand which actions are permissible and which are prohibited, even without explicit commands. We refer to the user-guided AI as passive intelligence and the unguided AI as active intelligence. This paper introduces RobotEQ, the first benchmark for active intelligence, aiming to assess whether existing models can comprehend and adhere to social norms in embodied scenarios. First, we construct RobotEQ-Data, a dataset consisting of 1,900 egocentric images, spanning 10 representative embodied categories and 56 subcategories. Through extensive manual annotation, we provide 5,353 action judgment questions and 1,286 spatial grounding questions, specifying appropriate robot actions across diverse scenarios. Furthermore, we establish RobotEQ-Bench to evaluate the performance of state-of-the-art models on this task. Experimental results show that current models still fall short in achieving reliable active intelligence, particularly in spatial grounding. Meanwhile, we observe that leveraging RAG techniques to incorporate external social norm knowledge bases can generally enhance performance. This work can facilitate the transition of robotics from user-guided passive manipulation to active social compliance.

Shufan Zhang, Hu Xiong

Vote-buying and voter-coercion are the impending threats when deploying remote online voting into large scale elections. With a policy of carrot and stick, it will encourage voters to deviate from honest voting strategy and spoil the democratic election. To deal with this problem, many voting protocols proposed their solutions with the notion of receipt-freeness. However, existing receipt-free voting protocols either rely on some impractical assumptions as untappable communication channel, or are burden with heavy voter-side computation and quadratic tallying complexity. In this paper, we present $Laoco\ddot{o}n$, a brand new cryptographic voting protocol which is practical and light-weight to be deployed in large scale online elections. By taking advantage of proxy re-encryption, our protocol can defend vote-buying attacks. Furthermore, we introduce a new property, candidate-adaptiveness, in electronic voting which refers to as every candidate knows the real-time vote number towards himself, while he knows nothing about others, nor he buys votes. We prove the correctness of our protocol and evaluate the performance with experimental results. Finally we advance some open problems which will be coped in our future work.