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The integration of Differential Privacy (DP) with diffusion models (DMs) presents a promising yet challenging frontier, particularly due to the substantial memorization capabilities of DMs that pose significant privacy risks. Differential privacy offers a rigorous framework for safeguarding individual data points during model training, with Differential Privacy Stochastic Gradient Descent (DP-SGD) being a prominent implementation. Diffusion method decomposes image generation into iterative steps, theoretically aligning well with DP's incremental noise addition. Despite the natural fit, the unique architecture of DMs necessitates tailored approaches to effectively balance privacy-utility trade-off. Recent developments in this field have highlighted the potential for generating high-quality synthetic data by pre-training on public data (i.e., ImageNet) and fine-tuning on private data, however, there is a pronounced gap in research on optimizing the trade-offs involved in DP settings, particularly concerning parameter efficiency and model scalability. Our work addresses this by proposing a parameter-efficient fine-tuning strategy optimized for private diffusion models, which minimizes the number of trainable parameters to enhance the privacy-utility trade-off. We empirically demonstrate that our method achieves state-of-the-art performance in DP synthesis, significantly surpassing previous benchmarks on widely studied datasets (e.g., with only 0.47M trainable parameters, achieving a more than 35% improvement over the previous state-of-the-art with a small privacy budget on the CelebA-64 dataset). Anonymous codes available at https://anonymous.4open.science/r/DP-LORA-F02F., Comment: 16 pages, 5 figures, 11 tables

While large language models (LLMs) such as Llama-2 or GPT-4 have shown impressive zero-shot performance, fine-tuning is still necessary to enhance their performance for customized datasets, domain-specific tasks, or other private needs. However, fine-tuning all parameters of LLMs requires significant hardware resources, which can be impractical for typical users. Therefore, parameter-efficient fine-tuning such as LoRA have emerged, allowing users to fine-tune LLMs without the need for considerable computing resources, with little performance degradation compared to fine-tuning all parameters. Unfortunately, recent studies indicate that fine-tuning can increase the risk to the safety of LLMs, even when data does not contain malicious content. To address this challenge, we propose Safe LoRA, a simple one-liner patch to the original LoRA implementation by introducing the projection of LoRA weights from selected layers to the safety-aligned subspace, effectively reducing the safety risks in LLM fine-tuning while maintaining utility. It is worth noting that Safe LoRA is a training-free and data-free approach, as it only requires the knowledge of the weights from the base and aligned LLMs. Our extensive experiments demonstrate that when fine-tuning on purely malicious data, Safe LoRA retains similar safety performance as the original aligned model. Moreover, when the fine-tuning dataset contains a mixture of both benign and malicious data, Safe LoRA mitigates the negative effect made by malicious data while preserving performance on downstream tasks.

Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model. In this work, we explore the benefits of VP in constructing compelling neural network classifiers with differential privacy (DP). We explore and integrate VP into canonical DP training methods and demonstrate its simplicity and efficiency. In particular, we discover that VP in tandem with PATE, a state-of-the-art DP training method that leverages the knowledge transfer from an ensemble of teachers, achieves the state-of-the-art privacy-utility trade-off with minimum expenditure of privacy budget. Moreover, we conduct additional experiments on cross-domain image classification with a sufficient domain gap to further unveil the advantage of VP in DP. Lastly, we also conduct extensive ablation studies to validate the effectiveness and contribution of VP under DP consideration. Our code is available at (https://github.com/EzzzLi/Prompt-PATE)., Comment: Published at ICCV 2023

Diffusion models for text-to-image (T2I) synthesis, such as Stable Diffusion (SD), have recently demonstrated exceptional capabilities for generating high-quality content. However, this progress has raised several concerns of potential misuse, particularly in creating copyrighted, prohibited, and restricted content, or NSFW (not safe for work) images. While efforts have been made to mitigate such problems, either by implementing a safety filter at the evaluation stage or by fine-tuning models to eliminate undesirable concepts or styles, the effectiveness of these safety measures in dealing with a wide range of prompts remains largely unexplored. In this work, we aim to investigate these safety mechanisms by proposing one novel concept retrieval algorithm for evaluation. We introduce Ring-A-Bell, a model-agnostic red-teaming tool for T2I diffusion models, where the whole evaluation can be prepared in advance without prior knowledge of the target model. Specifically, Ring-A-Bell first performs concept extraction to obtain holistic representations for sensitive and inappropriate concepts. Subsequently, by leveraging the extracted concept, Ring-A-Bell automatically identifies problematic prompts for diffusion models with the corresponding generation of inappropriate content, allowing the user to assess the reliability of deployed safety mechanisms. Finally, we empirically validate our method by testing online services such as Midjourney and various methods of concept removal. Our results show that Ring-A-Bell, by manipulating safe prompting benchmarks, can transform prompts that were originally regarded as safe to evade existing safety mechanisms, thus revealing the defects of the so-called safety mechanisms which could practically lead to the generation of harmful contents. Our codes are available at https://github.com/chiayi-hsu/Ring-A-Bell., Comment: This paper has already been accepted by ICLR 2024. This version is the camera-ready version

Recently, quantum classifiers have been found to be vulnerable to adversarial attacks, in which quantum classifiers are deceived by imperceptible noises, leading to misclassification. In this paper, we propose the first theoretical study demonstrating that adding quantum random rotation noise can improve robustness in quantum classifiers against adversarial attacks. We link the definition of differential privacy and show that the quantum classifier trained with the natural presence of additive noise is differentially private. Finally, we derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples, supported by experimental results simulated with noises from IBM's 7-qubits device., Comment: Accepted to IEEE ICASSP 2023

Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks. In this paper, we provide the first integral study and analysis for feed-forward neural networks in terms of the robustness in pairwise class margin and its generalization behavior under weight perturbation. We further design a new theory-driven loss function for training generalizable and robust neural networks against weight perturbations. Empirical experiments are conducted to validate our theoretical analysis. Our results offer fundamental insights for characterizing the generalization and robustness of neural networks against weight perturbations., Comment: This version has been accepted for poster presentation at NeurIPS 2021

Adversarial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. While being critical, we argue that solving this singular issue alone fails to provide a comprehensive robustness assessment. Even worse, the conclusions drawn from singular robustness may give a false sense of overall model robustness. Specifically, our findings show that adversarially trained models that are robust to input perturbations are still (or even more) vulnerable to weight perturbations when compared to standard models. In this paper, we formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights. To our best knowledge, this study is the first work considering simultaneous input-weight adversarial perturbations. Based on a multi-layer feed-forward neural network model with ReLU activation functions and standard classification loss, we establish error analysis for quantifying the loss sensitivity subject to $\ell_\infty$-norm bounded perturbations on data inputs and model weights. Based on the error analysis, we propose novel regularization functions for robust training and demonstrate improved non-singular robustness against joint input-weight adversarial perturbations.

Selective-area growth (SAG) of single-crystal wurtzite GaN nanorods (NRs) directly onto Si(001) substrates with un-etched native SiOx amorphous layer, assisted by a patterning TiNx mask fabricated by nanosphere lithography (NSL), has been realized by reactive magnetron sputter epitaxy (MSE). The GaN NRs were grown vertically to the substrate surface with the growth direction along c-axis in the well-defined nano-opening areas. A 5-step structural and morphological evolution of the SAG NRs observed at different sputtering times depicts a comprehensive growth model, listed in sequence as: formation of a polycrystalline wetting layer, predominating c-axis oriented nucleation, coarsening and coalescence of multi-islands, single NR evolution, and finally quasi-equilibrium crystal shape formation. Room-temperature cathodoluminescence spectroscopy shows a strong GaN bandedge emission with a uniform luminescence across the NRs, indicating that the SAG NRs are grown with high quality and purity. In addition, single-longitudinal-mode lasing, attributed to well-faceted NR geometry forming a Fabry-Perot cavity, was achieved by optical pumping, paving a way for fabricating high-performance laser optoelectronics using MSE., Funding Agencies|Swedish Research Council (VR) [621-2012-4420, 621-2013- 5360]; Swedish Governmental Agency for Innovation Systems (VINNOVA) under the VINNMER international qualification program; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [SFO-Mat-LiU 2009-00971]; Swedish Foundation for Strategic Research (SSF) through the Research Infrastructure Fellow [RIF 14-0074]; Knut and Alice Wallenberg Foundation; Myfab National Access program at MC2 NanoFabrication Laboratory, Chalmers University of Technology; Ministry of Science and Technology (MOST) of Taiwan [MOST 105-2112-M-006-004-MY3]

Selective-area growth (SAG) of single-crystal wurtzite GaN nanorods (NRs) directly onto Si(001) substrates with un-etched native SiOx amorphous layer, assisted by a patterning TiNx mask fabricated by nanosphere lithography (NSL), has been realized by reactive magnetron sputter epitaxy (MSE). The GaN NRs were grown vertically to the substrate surface with the growth direction along c-axis in the well-defined nano-opening areas. A 5-step structural and morphological evolution of the SAG NRs observed at different sputtering times depicts a comprehensive growth model, listed in sequence as: formation of a polycrystalline wetting layer, predominating c-axis oriented nucleation, coarsening and coalescence of multi-islands, single NR evolution, and finally quasi-equilibrium crystal shape formation. Room-temperature cathodoluminescence spectroscopy shows a strong GaN bandedge emission with a uniform luminescence across the NRs, indicating that the SAG NRs are grown with high quality and purity. In addition, single-longitudinal-mode lasing, attributed to well-faceted NR geometry forming a Fabry-Perot cavity, was achieved by optical pumping, paving a way for fabricating high-performance laser optoelectronics using MSE., Funding Agencies|Swedish Research Council (VR) [621-2012-4420, 621-2013- 5360]; Swedish Governmental Agency for Innovation Systems (VINNOVA) under the VINNMER international qualification program; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [SFO-Mat-LiU 2009-00971]; Swedish Foundation for Strategic Research (SSF) through the Research Infrastructure Fellow [RIF 14-0074]; Knut and Alice Wallenberg Foundation; Myfab National Access program at MC2 NanoFabrication Laboratory, Chalmers University of Technology; Ministry of Science and Technology (MOST) of Taiwan [MOST 105-2112-M-006-004-MY3]

Selective-area growth (SAG) of single-crystal wurtzite GaN nanorods (NRs) directly onto Si(001) substrates with un-etched native SiOx amorphous layer, assisted by a patterning TiNx mask fabricated by nanosphere lithography (NSL), has been realized by reactive magnetron sputter epitaxy (MSE). The GaN NRs were grown vertically to the substrate surface with the growth direction along c-axis in the well-defined nano-opening areas. A 5-step structural and morphological evolution of the SAG NRs observed at different sputtering times depicts a comprehensive growth model, listed in sequence as: formation of a polycrystalline wetting layer, predominating c-axis oriented nucleation, coarsening and coalescence of multi-islands, single NR evolution, and finally quasi-equilibrium crystal shape formation. Room-temperature cathodoluminescence spectroscopy shows a strong GaN bandedge emission with a uniform luminescence across the NRs, indicating that the SAG NRs are grown with high quality and purity. In addition, single-longitudinal-mode lasing, attributed to well-faceted NR geometry forming a Fabry-Perot cavity, was achieved by optical pumping, paving a way for fabricating high-performance laser optoelectronics using MSE., Funding Agencies|Swedish Research Council (VR) [621-2012-4420, 621-2013- 5360]; Swedish Governmental Agency for Innovation Systems (VINNOVA) under the VINNMER international qualification program; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [SFO-Mat-LiU 2009-00971]; Swedish Foundation for Strategic Research (SSF) through the Research Infrastructure Fellow [RIF 14-0074]; Knut and Alice Wallenberg Foundation; Myfab National Access program at MC2 NanoFabrication Laboratory, Chalmers University of Technology; Ministry of Science and Technology (MOST) of Taiwan [MOST 105-2112-M-006-004-MY3]

Selective-area growth (SAG) of single-crystal wurtzite GaN nanorods (NRs) directly onto Si(001) substrates with un-etched native SiOx amorphous layer, assisted by a patterning TiNx mask fabricated by nanosphere lithography (NSL), has been realized by reactive magnetron sputter epitaxy (MSE). The GaN NRs were grown vertically to the substrate surface with the growth direction along c-axis in the well-defined nano-opening areas. A 5-step structural and morphological evolution of the SAG NRs observed at different sputtering times depicts a comprehensive growth model, listed in sequence as: formation of a polycrystalline wetting layer, predominating c-axis oriented nucleation, coarsening and coalescence of multi-islands, single NR evolution, and finally quasi-equilibrium crystal shape formation. Room-temperature cathodoluminescence spectroscopy shows a strong GaN bandedge emission with a uniform luminescence across the NRs, indicating that the SAG NRs are grown with high quality and purity. In addition, single-longitudinal-mode lasing, attributed to well-faceted NR geometry forming a Fabry-Perot cavity, was achieved by optical pumping, paving a way for fabricating high-performance laser optoelectronics using MSE., Funding Agencies|Swedish Research Council (VR) [621-2012-4420, 621-2013- 5360]; Swedish Governmental Agency for Innovation Systems (VINNOVA) under the VINNMER international qualification program; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [SFO-Mat-LiU 2009-00971]; Swedish Foundation for Strategic Research (SSF) through the Research Infrastructure Fellow [RIF 14-0074]; Knut and Alice Wallenberg Foundation; Myfab National Access program at MC2 NanoFabrication Laboratory, Chalmers University of Technology; Ministry of Science and Technology (MOST) of Taiwan [MOST 105-2112-M-006-004-MY3]

Selective-area growth (SAG) of single-crystal wurtzite GaN nanorods (NRs) directly onto Si(001) substrates with un-etched native SiOx amorphous layer, assisted by a patterning TiNx mask fabricated by nanosphere lithography (NSL), has been realized by reactive magnetron sputter epitaxy (MSE). The GaN NRs were grown vertically to the substrate surface with the growth direction along c-axis in the well-defined nano-opening areas. A 5-step structural and morphological evolution of the SAG NRs observed at different sputtering times depicts a comprehensive growth model, listed in sequence as: formation of a polycrystalline wetting layer, predominating c-axis oriented nucleation, coarsening and coalescence of multi-islands, single NR evolution, and finally quasi-equilibrium crystal shape formation. Room-temperature cathodoluminescence spectroscopy shows a strong GaN bandedge emission with a uniform luminescence across the NRs, indicating that the SAG NRs are grown with high quality and purity. In addition, single-longitudinal-mode lasing, attributed to well-faceted NR geometry forming a Fabry-Perot cavity, was achieved by optical pumping, paving a way for fabricating high-performance laser optoelectronics using MSE., Funding Agencies|Swedish Research Council (VR) [621-2012-4420, 621-2013- 5360]; Swedish Governmental Agency for Innovation Systems (VINNOVA) under the VINNMER international qualification program; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [SFO-Mat-LiU 2009-00971]; Swedish Foundation for Strategic Research (SSF) through the Research Infrastructure Fellow [RIF 14-0074]; Knut and Alice Wallenberg Foundation; Myfab National Access program at MC2 NanoFabrication Laboratory, Chalmers University of Technology; Ministry of Science and Technology (MOST) of Taiwan [MOST 105-2112-M-006-004-MY3]