Lichun Li

Kai Yao, Zhaorui Tan, Tiandi Ye et al.

Offsite-tuning is a privacy-preserving method for tuning large language models (LLMs) by sharing a lossy compressed emulator from the LLM owners with data owners for downstream task tuning. This approach protects the privacy of both the model and data owners. However, current offsite tuning methods often suffer from adaptation degradation, high computational costs, and limited protection strength due to uniformly dropping LLM layers or relying on expensive knowledge distillation. To address these issues, we propose ScaleOT, a novel privacy-utility-scalable offsite-tuning framework that effectively balances privacy and utility. ScaleOT introduces a novel layerwise lossy compression algorithm that uses reinforcement learning to obtain the importance of each layer. It employs lightweight networks, termed harmonizers, to replace the raw LLM layers. By combining important original LLM layers and harmonizers in different ratios, ScaleOT generates emulators tailored for optimal performance with various model scales for enhanced privacy protection. Additionally, we present a rank reduction method to further compress the original LLM layers, significantly enhancing privacy with negligible impact on utility. Comprehensive experiments show that ScaleOT can achieve nearly lossless offsite tuning performance compared with full fine-tuning while obtaining better model privacy.

Farhan M. Aziz, Lichun Li, Jeff S. Shamma et al.

LTE/LTE-Advanced networks are known to be vulnerable to denial-of-service and loss-of-service attacks from smart jammers. In this article, the interaction between a smart jammer and LTE network is modeled as an infinite-horizon, zero-sum, asymmetric repeated game. The smart jammer and eNode B are modeled as the informed and the uninformed player, respectively. The main purpose of this article is to construct efficient suboptimal strategies for both players that can be used to solve the above-mentioned infinite-horizon repeated game with asymmetric and incomplete information. It has been shown in game-theoretic literature that security strategies provide optimal solution in zero-sum games. It is also shown that both players' security strategies in an infinite-horizon asymmetric game depend only on the history of the informed player's actions. However, fixed-sized sufficient statistics are needed for both players to solve the above-mentioned game efficiently. The smart jammer uses its evolving belief state as the fixed-sized sufficient statistics for the repeated game. Whereas, the LTE network (uninformed player) uses worst-case regret of its security strategy and its anti-discounted update as the fixed-sized sufficient statistics. Although fixed-sized sufficient statistics are employed by both players, optimal security strategy computation in λ-discounted asymmetric games is still hard to perform because of non-convexity. Hence, the problem is convexified in this article by devising `approximated' security strategies for both players that are based on approximated optimal game value. However, `approximated' strategies require full monitoring. Therefore, a simplistic yet effective `expected' strategy is also constructed for the LTE network that does not require full monitoring. The simulation results show that the smart jammer plays non-revealing and misleading strategies.