Inside the class of REGEX Languages

We study different possibilities of combining the concept of homomorphic replacement with regular expressions in order to investigate the class of languages given by extended regular expressions with backreferences (REGEX). It is shown in which regard existing and natural ways to do this fail to reach the expressive power of REGEX. Furthermore, the complexity of the membership problem for REGEX with a bounded number of backreferences is considered

Implementing Homomorphic Encryption Based Secure Feedback Control for Physical Systems

This paper is about an encryption based approach to the secure implementation of feedback controllers for physical systems. Specifically, Paillier's homomorphic encryption is used to digitally implement a class of linear dynamic controllers, which includes the commonplace static gain and PID type feedback control laws as special cases. The developed implementation is amenable to Field Programmable Gate Array (FPGA) realization. Experimental results, including timing analysis and resource usage characteristics for different encryption key lengths, are presented for the realization of an inverted pendulum controller; as this is an unstable plant, the control is necessarily fast

Formalising Confluence in PVS

Confluence is a critical property of computational systems which is related with determinism and non ambiguity and thus with other relevant computational attributes of functional specifications and rewriting system as termination and completion. Several criteria have been explored that guarantee confluence and their formalisations provide further interesting information. This work discusses topics and presents personal positions and views related with the formalisation of confluence properties in the Prototype Verification System PVS developed at our research group.Comment: In Proceedings DCM 2015, arXiv:1603.0053

LLMs Can Understand Encrypted Prompt: Towards Privacy-Computing Friendly Transformers

Prior works have attempted to build private inference frameworks for transformer-based large language models (LLMs) in a server-client setting, where the server holds the model parameters and the client inputs the private data for inference. However, these frameworks impose significant overhead when the private inputs are forward propagated through the original LLMs. In this paper, we show that substituting the computation- and communication-heavy operators in the transformer architecture with privacy-computing friendly approximations can greatly reduce the private inference costs with minor impact on model performance. Compared to the state-of-the-art Iron (NeurIPS 2022), our privacy-computing friendly model inference pipeline achieves a $5\times$ acceleration in computation and an 80\% reduction in communication overhead, while retaining nearly identical accuracy