Learn how Zero-Knowledge Proofs (ZKP) provide verifiable tool execution for Model Context Protocol (MCP) in a post-quantum world. Secure your AI infrastructure today.
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How Can Non-Human Identities Shape Data Security? Have you ever wondered how organizations manage to keep their sensitive data secure in rising cybersecurity threats? With digital expanding rapidly, handling sensitive data securely has never been more crucial. One key player is the management of Non-Human Identities (NHIs), especially in cloud environments. Cybersecurity experts have long […]
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Are Machine Identities the Missing Link in Financial Services Security? What role do machine identities, often referred to as Non-Human Identities (NHIs), play in ensuring the robustness of protected data exchanges? Where digital transformation is both a boon and a challenge, machine identities have become pivotal for a seamless yet secure operation across various sectors, […]
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How Are Non-Human Identities Transforming Cybersecurity in Healthcare? What happens when machine identities outstrip human ones in a healthcare setting? The proliferation of Non-Human Identities (NHIs) in cybersecurity is reshaping how we think about securing medical records. While these machine identities—secure passports if you will—navigate our digital environments, they reveal both vulnerabilities and opportunities. Understanding […]
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The Role of NHIs in Compliance Frameworks: What Do Companies Think? Are organizations truly satisfied with integrating Non-Human Identities (NHIs) into their compliance frameworks? The conversation around machine identities and their management in cybersecurity is gaining momentum, especially given the rapid shift to cloud environments. Companies across sectors like finance, healthcare, and travel are keen […]
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Session 12C: Membership Inference
Authors, Creators & Presenters:
PAPER
Yuefeng Peng (University of Massachusetts Amherst), Ali Naseh (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)
Deep learning models, while achieving remarkable performances across various tasks, are vulnerable to membership inference attacks (MIAs), wherein adversaries identify if a specific data point was part of the model's training set. This susceptibility raises substantial privacy concerns, especially when models are trained on sensitive datasets. Although various defenses have been proposed, there is still substantial room for improvement in the privacy-utility trade-off. In this work, we introduce a novel defense framework against MIAs by leveraging generative models. The key intuition of our defense is to *remove the differences between member and non-member inputs*, which is exploited by MIAs, by re-generating input samples before feeding them to the target model. Therefore, our defense, called Diffence, works *pre inference*, which is unlike prior defenses that are either training-time (modify the model) or post-inference time (modify the model's output). A unique feature of Diffence is that it works on input samples only, without modifying the training or inference phase of the target model. Therefore, it can be cascaded with other defense mechanisms as we demonstrate through experiments. Diffence is specifically designed to preserve the model's prediction labels for each sample, thereby not affecting accuracy. Furthermore, we have empirically demonstrated that it does not reduce the usefulness of the confidence vectors. Through extensive experimentation, we show that Diffence can serve as a robust plug-n-play defense mechanism, enhancing membership privacy without compromising model utility--both in terms of accuracy and the usefulness of confidence vectors--across standard and defended settings. For instance, Diffence reduces MIA attack accuracy against an undefended model by 15.8% and attack AUC by 14.0% on average across three datasets, all without impacting model utility. By integrating Diffence with prior defenses, we can achieve new state-of-the-art performances in the privacy-utility trade-off. For example, when combined with the state-of-the-art SELENA defense it reduces attack accuracy by 9.3%, and attack AUC by 10.0%. Diffence achieves this by imposing a negligible computation overhead, adding only 57ms to the inference time per sample processed on average.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – Diffence: Fencing Membership Privacy With Diffusion Models appeared first on Security Boulevard.
How Can We Streamline NHI Lifecycle Management for Better Cloud Security? What if you could seamlessly integrate non-human identity management into your cybersecurity strategy to bolster cloud security across your organization? With digital transforms rapidly, the importance of proactive NHI lifecycle management becomes ever more crucial. Let’s explore how organizations can optimize NHI lifecycle management […]
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Can Non-Human Identities Reinvent Cybersecurity with Agentic AI? What if the key to fortifying cybersecurity lies not in more layers of defense, but in effectively managing the machine identities that already exist within your organization’s infrastructure? Enter Non-Human Identities (NHIs)—the machine identities that are increasingly important. Understanding Non-Human Identities NHIs function much like machine passports. […]
The post Are there guaranteed cybersecurity benefits with Agentic AI implementation? appeared first on Entro.
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How Does Non-Human Identity Management Enhance Identity Security Assurance? How can organizations bolster their identity security assurance amidst the growing complexity of cloud environments and the proliferation of machine identities? The answer lies in adopting Non-Human Identities (NHIs). This approach is increasingly crucial for diverse industries, providing a comprehensive solution to the security challenges posed […]
The post How assured is identity security with the adoption of NHIs? appeared first on Entro.
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Why Should IT Managers Prioritize Non-Human Identities and Secrets Security Management? How well do you know the invisible workforce within your organization? No, it’s not the human workforce that charms at meetings or brainstorms ideas in conference rooms. Instead, it’s the machine identities—also known as Non-Human Identities (NHIs)—that silently perform myriad tasks, from managing applications […]
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Identity risk scoring has become a critical input for fraud prevention, security operations, and trust decisions. Organizations increasingly rely on risk scores to decide when to step up authentication, block access, or flag activity for investigation. But despite widespread adoption, many identity risk programs struggle with the same problem: Risk scores are generated, but teams …
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Session 12C: Membership Inference
Authors, Creators & Presenters: Yan Pang (University of Virginia), Tianhao Wang (University of Virginia)
PAPER
Black-box Membership Inference Attacks against Fine-tuned Diffusion Models
With the rapid advancement of diffusion-based image-generative models, the quality of generated images has become increasingly photorealistic. Moreover, with the release of high-quality pre-trained image-generative models, a growing number of users are downloading these pre-trained models to fine-tune them with downstream datasets for various image-generation tasks. However, employing such powerful pre-trained models in downstream tasks presents significant privacy leakage risks. In this paper, we propose the first scores-based membership inference attack framework tailored for recent diffusion models, and in the more stringent black-box access setting. Considering four distinct attack scenarios and three types of attacks, this framework is capable of targeting any popular conditional generator model, achieving high precision, evidenced by an impressive AUC of 0.95.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – Black-Box Membership Inference Attacks Against Fine-Tuned Diffusion Models appeared first on Security Boulevard.
Part 3 of a 4-Part Series: The SEG Breakup Guide for MSPs
The post The Relationship Tax: What Staying With Your SEG Is Really Costing Your MSP appeared first on Security Boulevard.
Cyber threats are growing at an unprecedented pace. In 2024 alone, global cyber threat losses reached an estimated US$9.5 trillion, and this figure is projected to rise even further in 2025. If threats were a country, it would rank as the world’s third-largest economy, behind only the United States and China. As attackers increasingly leverage […]
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MSSPs optimize for SLA metrics, not security outcomes. Autonomous SOC platforms like Morpheus can replace them at 10x lower cost.
The post From MSSP to Autonomous SOC: Replacing Linear Headcount with Infinite Compute appeared first on D3 Security.
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How Can Non-Human Identities (NHIs) Transform Scalable Security for Large Enterprises? One might ask: how can large enterprises ensure scalable security without compromising on efficiency and compliance? The answer lies in the effective management of Non-Human Identities (NHIs) and secrets security management. With machine identities, NHIs are pivotal in crafting a robust security framework, especially […]
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How Secure Is Your Organization’s Cloud Environment? How secure is your organization’s cloud environment? With the digital transformation accelerates, gaps in security are becoming increasingly noticeable. Non-Human Identities (NHIs), representing machine identities, are pivotal in these frameworks. In cybersecurity, they are formed by integrating a ‘Secret’—like an encrypted password or key—and the permissions allocated by […]
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How do Non-Human Identities Shape the Future of Cloud Security? Have you ever wondered how machine identities influence cloud security? Non-Human Identities (NHIs) are crucial for maintaining robust cybersecurity frameworks, especially in cloud environments. These identities demand a sophisticated understanding, when they are essential for secure interactions between machines and their environments. The Critical Role […]
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What Makes Non-Human Identities Essential for Cloud Compliance Auditing? With cybersecurity threats evolve, how can organizations ensure their compliance measures are robust enough to handle the complexities of modern cloud environments? The answer lies in understanding and managing Non-Human Identities (NHIs)—a crucial component for establishing a secure and compliant framework in cloud computing. Understanding NHIs: […]
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A survey of 512 cybersecurity professionals finds 76% report that over half (54%) of the security incidents that occurred in the past 12 months involved some issue relating to identity management. Conducted by Permiso Security, a provider of an identity security platform, the survey also finds 95% are either very confident (52%) or somewhat confident..
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