arXiv subjects

Rayne Holland

Publications and source records attributed to Rayne Holland.

8 recordsLinked to original sources

arXiv2026

Fast and Optimal Differentially Private Frequent-Substring Mining

Given a dataset of $n$ user-contributed strings, each of length at most $\ell$, a key problem is how to identify all frequent substrings while preserving each user's privacy. Recent work by Bernardini et al. (PODS'25) introduced a $\varepsilon$-differentially private algorithm achieving near-optimal error, but at the prohibitive cost of $O(n^2\ell^4)$ space and processing time. In this work, we present a new $\varepsilon$-differentially private algorithm that retains the same near-optimal error guarantees while reducing space complexity to $O(n \ell+ |\Sigma| )$ and time complexity to $O(n \ell\log |\Sigma| + |\Sigma| )$, for input alphabet $\Sigma$. Our approach builds on a top-down exploration of candidate substrings but introduces two new innovations: (i) a refined candidate-generation strategy that leverages the structural properties of frequent prefixes and suffixes, and (ii) pruning of the search space guided by frequency relations. These techniques eliminate the quadratic blow-ups inherent in prior work, enabling scalable frequent substring mining under differential privacy.

cs.DS↗

arXiv2025

An Iconic Heavy Hitter Algorithm Made Private

Identifying heavy hitters in data streams is a fundamental problem with widespread applications in modern analytics systems. These streams are often derived from sensitive user activity, making update-level privacy guarantees necessary. While recent work has adapted the classical heavy hitter algorithm Misra-Gries to satisfy differential privacy in the streaming model, the privatization of other heavy hitter algorithms with better empirical utility is absent. Under this observation, we present the first differentially private variant of the SpaceSaving algorithm, which, in the non-private setting, is regarded as the state-of-the-art in practice. Our construction post-processes a non-private SpaceSaving summary by injecting asymptotically optimal noise and applying a carefully calibrated selection rule that suppresses unstable labels. This yields strong privacy guarantees while preserving the empirical advantages of SpaceSaving. Second, we introduce a generic method for extracting heavy hitters from any differentially private frequency oracle in the data stream model. The method requires only O(k) additional memory, where k is the number of heavy items, and provides a mechanism for safely releasing item identities from noisy frequency estimates. This yields an efficient, plug-and-play approach for private heavy hitter recovery from linear sketches. Finally, we conduct an experimental evaluation on synthetic and real-world datasets. Across a wide range of privacy parameters and space budgets, our method provides superior utility to the existing differentially private Misra-Gries algorithm. Our results demonstrate that the empirical superiority of SpaceSaving survives privatization and that efficient, practical heavy hitter identification is achievable under strong differential privacy guarantees.

cs.CR↗

arXiv2025

TELSAFE: Security Gap Quantitative Risk Assessment Framework

Gaps between established security standards and their practical implementation have the potential to introduce vulnerabilities, possibly exposing them to security risks. To effectively address and mitigate these security and compliance challenges, security risk management strategies are essential. However, it must adhere to well-established strategies and industry standards to ensure consistency, reliability, and compatibility both within and across organizations. In this paper, we introduce a new hybrid risk assessment framework called TELSAFE, which employs probabilistic modeling for quantitative risk assessment and eliminates the influence of expert opinion bias. The framework encompasses both qualitative and quantitative assessment phases, facilitating effective risk management strategies tailored to the unique requirements of organizations. A specific use case utilizing Common Vulnerabilities and Exposures (CVE)-related data demonstrates the framework's applicability and implementation in real-world scenarios, such as in the telecommunications industry.

cs.CR↗

arXiv2025

Scalable Differentially Private Sketches under Continual Observation

Linear sketches are fundamental tools in data stream analytics. They are notable for supporting both approximate frequency queries and heavy hitter detection with bounded trade-offs for error and memory. Importantly, on streams that contain sensitive information, linear sketches can be easily privatized with the injection of a suitable amount of noise. This process is efficient in the single release model, where the output is released only at the end of the stream. In this setting, it suffices to add noise to the sketch once. In contrast, in the continual observation model, where the output is released at every time-step, fresh noise needs to be added to the sketch before each release. This creates an additional computational overhead. To address this, we introduce Lazy Sketch, a novel differentially private sketching method that employs lazy updates, perturbing and modifying only a small portion of the sketch at each step. Compared to prior work, we reduce the update complexity by a factor of $O(w)$, where $w$ is the width of the sketch. Experiments demonstrate that our method increases throughput by up to 250x over prior work, making continual observation differential privacy practical for high-speed streaming applications.

cs.CR↗

arXiv2024

Elevating Software Trust: Unveiling and Quantifying the Risk Landscape

Considering the ever-evolving threat landscape and rapid changes in software development, we propose a risk assessment framework called SAFER (Software Analysis Framework for Evaluating Risk). This framework is based on the necessity of a dynamic, data-driven, and adaptable process to quantify security risk in the software supply chain. Usually, when formulating such frameworks, static pre-defined weights are assigned to reflect the impact of each contributing parameter while aggregating these individual parameters to compute resulting security risk scores. This leads to inflexibility, a lack of adaptability, and reduced accuracy, making them unsuitable for the changing nature of the digital world. We adopt a novel perspective by examining security risk through the lens of trust and incorporating the human aspect. Moreover, we quantify security risk associated with individual software by assessing and formulating risk elements quantitatively and exploring dynamic data-driven weight assignment. This enhances the sensitivity of the framework to cater to the evolving security risk factors associated with software development and the different actors involved in the entire process. The devised framework is tested through a dataset containing 9000 samples, comprehensive scenarios, assessments, and expert opinions. Furthermore, a comparison between scores computed by the OpenSSF scorecard, OWASP risk calculator, and the proposed SAFER framework has also been presented. The results suggest that SAFER mitigates subjectivity and yields dynamic data-driven weights as well as security risk scores.

cs.SE↗

arXiv2024

Private Synthetic Data Generation in Bounded Memory

We propose $\mathtt{PrivHP}$, a lightweight synthetic data generator with \textit{differential privacy} guarantees. $\mathtt{PrivHP}$ uses a novel hierarchical decomposition that approximates the input's cumulative distribution function (CDF) in bounded memory. It balances hierarchy depth, noise addition, and pruning of low-frequency subdomains while preserving frequent ones. Private sketches estimate subdomain frequencies efficiently without full data access. A key feature is the pruning parameter $k$, which controls the trade-off between space and utility. We define the skew measure $\mathtt{tail}_k$, capturing all but the top $k$ subdomain frequencies. Given a dataset $\mathcal{X}$, $\mathtt{PrivHP}$ uses $M=\mathcal{O}(k\log^2 |X|)$ space and, for input domain $\Omega = [0,1]$, ensures $\varepsilon$-differential privacy. It yields a generator with expected Wasserstein distance: \[ \mathcal{O}\left(\frac{\log^2 M}{\varepsilon n} + \frac{||\mathtt{tail}_k(\mathcal{X})||_1}{M n}\right) \] from the empirical distribution. This parameterized trade-off offers a level of flexibility unavailable in prior work. We also provide interpretable utility bounds that account for hierarchy depth, privacy noise, pruning, and frequency estimation errors.

cs.CR↗

arXiv2024

Attacking Slicing Network via Side-channel Reinforcement Learning Attack

Network slicing in 5G and the future 6G networks will enable the creation of multiple virtualized networks on a shared physical infrastructure. This innovative approach enables the provision of tailored networks to accommodate specific business types or industry users, thus delivering more customized and efficient services. However, the shared memory and cache in network slicing introduce security vulnerabilities that have yet to be fully addressed. In this paper, we introduce a reinforcement learning-based side-channel cache attack framework specifically designed for network slicing environments. Unlike traditional cache attack methods, our framework leverages reinforcement learning to dynamically identify and exploit cache locations storing sensitive information, such as authentication keys and user registration data. We assume that one slice network is compromised and demonstrate how the attacker can induce another shared slice to send registration requests, thereby estimating the cache locations of critical data. By formulating the cache timing channel attack as a reinforcement learning-driven guessing game between the attack slice and the victim slice, our model efficiently explores possible actions to pinpoint memory blocks containing sensitive information. Experimental results showcase the superiority of our approach, achieving a success rate of approximately 95\% to 98\% in accurately identifying the storage locations of sensitive data. This high level of accuracy underscores the potential risks in shared network slicing environments and highlights the need for robust security measures to safeguard against such advanced side-channel attacks.

cs.CR↗

arXiv2024

The Philosopher's Stone: Trojaning Plugins of Large Language Models

Open-source Large Language Models (LLMs) have recently gained popularity because of their comparable performance to proprietary LLMs. To efficiently fulfill domain-specialized tasks, open-source LLMs can be refined, without expensive accelerators, using low-rank adapters. However, it is still unknown whether low-rank adapters can be exploited to control LLMs. To address this gap, we demonstrate that an infected adapter can induce, on specific triggers,an LLM to output content defined by an adversary and to even maliciously use tools. To train a Trojan adapter, we propose two novel attacks, POLISHED and FUSION, that improve over prior approaches. POLISHED uses a superior LLM to align naïvely poisoned data based on our insight that it can better inject poisoning knowledge during training. In contrast, FUSION leverages a novel over-poisoning procedure to transform a benign adapter into a malicious one by magnifying the attention between trigger and target in model weights. In our experiments, we first conduct two case studies to demonstrate that a compromised LLM agent can use malware to control the system (e.g., a LLM-driven robot) or to launch a spear-phishing attack. Then, in terms of targeted misinformation, we show that our attacks provide higher attack effectiveness than the existing baseline and, for the purpose of attracting downloads, preserve or improve the adapter's utility. Finally, we designed and evaluated three potential defenses. However, none proved entirely effective in safeguarding against our attacks, highlighting the need for more robust defenses supporting a secure LLM supply chain.

cs.CR↗