arXiv subjects

Fatih Turkmen

Publications and source records attributed to Fatih Turkmen.

At least 19 recordsLinked to original sources

arXiv2026

Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey

Neuromorphic computing mimics brain-inspired mechanisms through spiking neurons and energy-efficient processing, offering a pathway to efficient in-memory computing (IMC). However, these advancements raise critical security and privacy concerns. As the adoption of bio-inspired architectures and memristive devices increases, so does the urgency to assess the vulnerability of these emerging technologies to hardware and software attacks. Emerging architectures introduce new attack surfaces, particularly due to asynchronous, event-driven processing and stochastic device behavior. The integration of memristors into neuromorphic hardware and software implementations in spiking neural networks offers diverse possibilities for advanced computing architectures, including their role in security-aware applications. This survey systematically analyzes the security landscape of neuromorphic systems, covering attack methodologies, side-channel vulnerabilities, and countermeasures. We focus on both hardware and software concerns relevant to spiking neural networks (SNNs) and hardware primitives, such as Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) for cryptographic and secure computation applications. We approach this analysis from diverse perspectives, from attack methodologies to countermeasure strategies that integrate efficiency and protection in brain-inspired hardware. This review not only maps the current landscape of security threats but provides a foundation for developing secure and trustworthy neuromorphic architectures.

cs.CR↗

arXiv2025

EL-MIA: Quantifying Membership Inference Risks of Sensitive Entities in LLMs

Membership inference attacks (MIA) aim to infer whether a particular data point is part of the training dataset of a model. In this paper, we propose a new task in the context of LLM privacy: entity-level discovery of membership risk focused on sensitive information (PII, credit card numbers, etc). Existing methods for MIA can detect the presence of entire prompts or documents in the LLM training data, but they fail to capture risks at a finer granularity. We propose the ``EL-MIA'' framework for auditing entity-level membership risks in LLMs. We construct a benchmark dataset for the evaluation of MIA methods on this task. Using this benchmark, we conduct a systematic comparison of existing MIA techniques as well as two newly proposed methods. We provide a comprehensive analysis of the results, trying to explain the relation of the entity level MIA susceptability with the model scale, training epochs, and other surface level factors. Our findings reveal that existing MIA methods are limited when it comes to entity-level membership inference of the sensitive attributes, while this susceptibility can be outlined with relatively straightforward methods, highlighting the need for stronger adversaries to stress test the provided threat model.

cs.LG↗

arXiv2025

Personal Attribute Leakage in Federated Speech Models

Federated learning is a common method for privacy-preserving training of machine learning models. In this paper, we analyze the vulnerability of ASR models to attribute inference attacks in the federated setting. We test a non-parametric white-box attack method under a passive threat model on three ASR models: Wav2Vec2, HuBERT, and Whisper. The attack operates solely on weight differentials without access to raw speech from target speakers. We demonstrate attack feasibility on sensitive demographic and clinical attributes: gender, age, accent, emotion, and dysarthria. Our findings indicate that attributes that are underrepresented or absent in the pre-training data are more vulnerable to such inference attacks. In particular, information about accents can be reliably inferred from all models. Our findings expose previously undocumented vulnerabilities in federated ASR models and offer insights towards improved security.

cs.CL↗

arXiv2025

BACFuzz: Exposing the Silence on Broken Access Control Vulnerabilities in Web Applications

Broken Access Control (BAC) remains one of the most critical and widespread vulnerabilities in web applications, allowing attackers to access unauthorized resources or perform privileged actions. Despite its severity, BAC is underexplored in automated testing due to key challenges: the lack of reliable oracles and the difficulty of generating semantically valid attack requests. We introduce BACFuzz, the first gray-box fuzzing framework specifically designed to uncover BAC vulnerabilities, including Broken Object-Level Authorization (BOLA) and Broken Function-Level Authorization (BFLA) in PHP-based web applications. BACFuzz combines LLM-guided parameter selection with runtime feedback and SQL-based oracle checking to detect silent authorization flaws. It employs lightweight instrumentation to capture runtime information that guides test generation, and analyzes backend SQL queries to verify whether unauthorized inputs flow into protected operations. Evaluated on 20 real-world web applications, including 15 CVE cases and 2 known benchmarks, BACFuzz detects 16 of 17 known issues and uncovers 26 previously unknown BAC vulnerabilities with low false positive rates. All identified issues have been responsibly disclosed, and artifacts will be publicly released.

cs.CR↗

arXiv2024

Undesirable Memorization in Large Language Models: A Survey

While recent research increasingly showcases the remarkable capabilities of Large Language Models (LLMs), it is equally crucial to examine their associated risks. Among these, privacy and security vulnerabilities are particularly concerning, posing significant ethical and legal challenges. At the heart of these vulnerabilities stands memorization, which refers to a model's tendency to store and reproduce phrases from its training data. This phenomenon has been shown to be a fundamental source to various privacy and security attacks against LLMs. In this paper, we provide a taxonomy of the literature on LLM memorization, exploring it across three dimensions: granularity, retrievability, and desirability. Next, we discuss the metrics and methods used to quantify memorization, followed by an analysis of the causes and factors that contribute to memorization phenomenon. We then explore strategies that are used so far to mitigate the undesirable aspects of this phenomenon. We conclude our survey by identifying potential research topics for the near future, including methods to balance privacy and performance, and the analysis of memorization in specific LLM contexts such as conversational agents, retrieval-augmented generation, and diffusion language models. Given the rapid research pace in this field, we also maintain a dedicated repository of the references discussed in this survey which will be regularly updated to reflect the latest developments.

cs.CL↗

arXiv2024

Collaborative CP-NIZKs: Modular, Composable Proofs for Distributed Secrets

Non-interactive zero-knowledge (NIZK) proofs of knowledge have proven to be highly relevant for securely realizing a wide array of applications that rely on both privacy and correctness. They enable a prover to convince any party of the correctness of a public statement for a secret witness. However, most NIZKs do not natively support proving knowledge of a secret witness that is distributed over multiple provers. Previously, collaborative proofs [51] have been proposed to overcome this limitation. We investigate the notion of composability in this setting, following the Commit-and-Prove design of LegoSNARK [17]. Composability allows users to combine different, specialized NIZKs (e.g., one arithmetic circuit, one boolean circuit, and one for range proofs) with the aim of reducing the prove generation time. Moreover, it opens the door to efficient realizations of many applications in the collaborative setting such as mutually exclusive prover groups, combining collaborative and single-party proofs and efficiently implementing publicly auditable MPC (PA-MPC). We present the first, general definition for collaborative commit-and-prove NIZK (CP-NIZK) proofs of knowledge and construct distributed protocols to enable their realization. We implement our protocols for two commonly used NIZKs, Groth16 and Bulletproofs, and evaluate their practicality in a variety of computational settings. Our findings indicate that composability adds only minor overhead, especially for large circuits. We experimented with our construction in an application setting, and when compared to prior works, our protocols reduce latency by 18-55x while requiring only a fraction (0.2%) of the communication.

cs.CR↗

arXiv2024

Efficient Verifiable Differential Privacy with Input Authenticity in the Local and Shuffle Model

Local differential privacy (LDP) enables the efficient release of aggregate statistics without having to trust the central server (aggregator), as in the central model of differential privacy, and simultaneously protects a client's sensitive data. The shuffle model with LDP provides an additional layer of privacy, by disconnecting the link between clients and the aggregator. However, LDP has been shown to be vulnerable to malicious clients who can perform both input and output manipulation attacks, i.e., before and after applying the LDP mechanism, to skew the aggregator's results. In this work, we show how to prevent malicious clients from compromising LDP schemes. Our only realistic assumption is that the initial raw input is authenticated; the rest of the processing pipeline, e.g., formatting the input and applying the LDP mechanism, may be under adversarial control. We give several real-world examples where this assumption is justified. Our proposed schemes for verifiable LDP (VLDP), prevent both input and output manipulation attacks against generic LDP mechanisms, requiring only one-time interaction between client and server, unlike existing alternatives [37, 43]. Most importantly, we are the first to provide an efficient scheme for VLDP in the shuffle model. We describe, and prove security of, two schemes for VLDP in the local model, and one in the shuffle model. We show that all schemes are highly practical, with client run times of less than 2 seconds, and server run times of 5-7 milliseconds per client.

cs.CR↗

arXiv2024

Fuzzing Frameworks for Server-side Web Applications: A Survey

There are around 5.3 billion Internet users, amounting to 65.7% of the global population, and web technology is the backbone of the services delivered via the Internet. To ensure web applications are free from security-related bugs, web developers test the server-side web applications before deploying them to production. The tests are commonly conducted through the interfaces (i.e., Web API) that the applications expose since they are the entry points to the application. Fuzzing is one of the most promising automated software testing techniques suitable for this task; however, the research on (server-side) web application fuzzing has been rather limited compared to binary fuzzing which is researched extensively. This study reviews the state-of-the-art fuzzing frameworks for testing web applications through web API, identifies open challenges, and gives potential future research. We collect papers from seven online repositories of peer-reviewed articles over the last ten years. Compared to other similar studies, our review focuses more deeply on revealing prior work strategies in generating valid HTTP requests, utilising feedback from the Web Under Tests (WUTs), and expanding input spaces. The findings of this survey indicate that several crucial challenges need to be solved, such as the ineffectiveness of web instrumentation and the complexity of handling microservice applications. Furthermore, some potential research directions are also provided, such as fuzzing for web client programming. Ultimately, this paper aims to give a good starting point for developing a better web fuzzing framework.

cs.SE↗

arXiv2024

Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness

Decentralized machine learning (DL) has been receiving an increasing interest recently due to the elimination of a single point of failure, present in Federated learning setting. Yet, it is threatened by the looming threat of Byzantine clients who intentionally disrupt the learning process by broadcasting arbitrary model updates to other clients, seeking to degrade the performance of the global model. In response, robust aggregation schemes have emerged as promising solutions to defend against such Byzantine clients, thereby enhancing the robustness of Decentralized Learning. Defenses against Byzantine adversaries, however, typically require access to the updates of other clients, a counterproductive privacy trade-off that in turn increases the risk of inference attacks on those same model updates. In this paper, we introduce SecureDL, a novel DL protocol designed to enhance the security and privacy of DL against Byzantine threats. SecureDL~facilitates a collaborative defense, while protecting the privacy of clients' model updates through secure multiparty computation. The protocol employs efficient computation of cosine similarity and normalization of updates to robustly detect and exclude model updates detrimental to model convergence. By using MNIST, Fashion-MNIST, SVHN and CIFAR-10 datasets, we evaluated SecureDL against various Byzantine attacks and compared its effectiveness with four existing defense mechanisms. Our experiments show that SecureDL is effective even in the case of attacks by the malicious majority (e.g., 80% Byzantine clients) while preserving high training accuracy.

cs.CR↗

arXiv2024

Privacy-Preserving, Dropout-Resilient Aggregation in Decentralized Learning

Decentralized learning (DL) offers a novel paradigm in machine learning by distributing training across clients without central aggregation, enhancing scalability and efficiency. However, DL's peer-to-peer model raises challenges in protecting against inference attacks and privacy leaks. By forgoing central bottlenecks, DL demands privacy-preserving aggregation methods to protect data from 'honest but curious' clients and adversaries, maintaining network-wide privacy. Privacy-preserving DL faces the additional hurdle of client dropout, clients not submitting updates due to connectivity problems or unavailability, further complicating aggregation. This work proposes three secret sharing-based dropout resilience approaches for privacy-preserving DL. Our study evaluates the efficiency, performance, and accuracy of these protocols through experiments on datasets such as MNIST, Fashion-MNIST, SVHN, and CIFAR-10. We compare our protocols with traditional secret-sharing solutions across scenarios, including those with up to 1000 clients. Evaluations show that our protocols significantly outperform conventional methods, especially in scenarios with up to 30% of clients dropout and model sizes of up to $10^6$ parameters. Our approaches demonstrate markedly high efficiency with larger models, higher dropout rates, and extensive client networks, highlighting their effectiveness in enhancing decentralized learning systems' privacy and dropout robustness.

cs.CR↗

arXiv2024

VPAS: Publicly Verifiable and Privacy-Preserving Aggregate Statistics on Distributed Datasets

Aggregate statistics play an important role in extracting meaningful insights from distributed data while preserving privacy. A growing number of application domains, such as healthcare, utilize these statistics in advancing research and improving patient care. In this work, we explore the challenge of input validation and public verifiability within privacy-preserving aggregation protocols. We address the scenario in which a party receives data from multiple sources and must verify the validity of the input and correctness of the computations over this data to third parties, such as auditors, while ensuring input data privacy. To achieve this, we propose the "VPAS" protocol, which satisfies these requirements. Our protocol utilizes homomorphic encryption for data privacy, and employs Zero-Knowledge Proofs (ZKP) and a blockchain system for input validation and public verifiability. We constructed VPAS by extending existing verifiable encryption schemes into secure protocols that enable N clients to encrypt, aggregate, and subsequently release the final result to a collector in a verifiable manner. We implemented and experimentally evaluated VPAS with regard to encryption costs, proof generation, and verification. The findings indicate that the overhead associated with verifiability in our protocol is 10x lower than that incurred by simply using conventional zkSNARKs. This enhanced efficiency makes it feasible to apply input validation with public verifiability across a wider range of applications or use cases that can tolerate moderate computational overhead associated with proof generation.

cs.CR↗

arXiv2023

Verifiable Privacy-Preserving Computing

Privacy-preserving computation (PPC) methods, such as secure multiparty computation (MPC) and homomorphic encryption (HE), are deployed increasingly often to guarantee data confidentiality in computations over private, distributed data. Similarly, we observe a steep increase in the adoption of zero-knowledge proofs (ZKPs) to guarantee (public) verifiability of locally executed computations. We project that applications that are data intensive and require strong privacy guarantees, are also likely to require verifiable correctness guarantees, especially when outsourced. While the combination of methods for verifiability and privacy protection has clear benefits, certain challenges stand before their widespread practical adoption. In this work, we analyze existing solutions that combine verifiability with privacy-preserving computations over distributed data, in order to preserve confidentiality and guarantee correctness at the same time. We classify and compare 37 different schemes, regarding solution approach, security, efficiency, and practicality. Lastly, we discuss some of the most promising solutions in this regard, and present various open challenges and directions for future research.

cs.CR↗

arXiv2023

SecFlow: Adaptive Security-Aware Workflow Management System in Multi-Cloud Environments

In this paper, we propose an architecture for a security-aware workflow management system (WfMS) we call SecFlow in answer to the recent developments of combining workflow management systems with Cloud environments and the still lacking abilities of such systems to ensure the security and privacy of cloud-based workflows. The SecFlow architecture focuses on full workflow life cycle coverage as, in addition to the existing approaches to design security-aware processes, there is a need to fill in the gap of maintaining security properties of workflows during their execution phase. To address this gap, we derive the requirements for such a security-aware WfMS and design a system architecture that meets these requirements. SecFlow integrates key functional components such as secure model construction, security-aware service selection, security violation detection, and adaptive response mechanisms while considering all potential malicious parties in multi-tenant and cloud-based WfMS.

cs.CR↗

arXiv2022

Security and Privacy Concerns in Cloud-based Scientific and Business Workflows: A Systematic Review

Today, the number of data-intensive and compute-intensive applications like business and scientific workflows has dramatically increased, which made cloud computing more popular in the matter of delivering a large amount of computing resources on demand. On the other hand, security is a critical issue affecting the wide adoption of cloud technologies, especially for workflows that are mostly dealing with sensitive data and tasks. In this paper, we carry out a review of the state-of-the-art on how security and privacy concerns in scientific and business workflows in cloud environments are being addressed and identify the limitations and gaps in the current body of knowledge in this area. In this extensive literature review, we first present a classification of the state-of-the-art security solutions organized according to the phases of the workflow life cycle they target. Based on our findings, we provide a detailed review and classification of the most relevant available literature focusing on the execution, monitoring, and adaptation phases of workflows. Finally, we present a list of open research issues related to the security of cloud-based workflows and discuss them.

cs.CR↗

arXiv2022

Effectiveness and Scalability of Fuzzing Techniques in CI/CD Pipelines

Fuzzing has proven to be a fundamental technique to automated software testing but also a costly one. With the increased adoption of CI/CD practices in software development, a natural question to ask is `What are the best ways to integrate fuzzing into CI/CD pipelines considering the velocity in code changes and the automated delivery/deployment practices?'. Indeed, a recent study by B\"ohme and Zhu shows that four in every five bugs have been introduced by recent code changes (i.e. regressions). In this paper, we take a close look at the integration of fuzzers to CI/CD pipelines from both automated software testing and continuous development angles. Firstly, we study an optimization opportunity to triage commits that do not require fuzzing and find, through experimental analysis, that the average fuzzing effort in CI/CD can be reduced by ~63% in three of the nine libraries we analyzed (>40% for six libraries). Secondly, we investigate the impact of fuzzing campaign duration on the CI/CD process: A shorter fuzzing campaign such as 15 minutes (as opposed to the wisdom of 24 hours in the field) facilitates a faster pipeline and can still uncover important bugs, but may also reduce its capability to detect sophisticated bugs. Lastly, we discuss a prioritization strategy that automatically assigns resources to fuzzing campaigns based on a set of predefined priority strategies. Our findings suggest that continuous fuzzing (as part of the automated testing in CI/CD) is indeed beneficial and there are many optimization opportunities to improve the effectiveness and scalability of fuzz testing.

cs.SE↗

arXiv2021

Blockchain for Genomics: A Systematic Literature Review

Human genomic data carry unique information about an individual and offer unprecedented opportunities for healthcare. The clinical interpretations derived from large genomic datasets can greatly improve healthcare and pave the way for personalized medicine. Sharing genomic datasets, however, pose major challenges, as genomic data is different from traditional medical data, indirectly revealing information about descendants and relatives of the data owner and carrying valid information even after the owner passes away. Therefore, stringent data ownership and control measures are required when dealing with genomic data. In order to provide secure and accountable infrastructure, blockchain technologies offer a promising alternative to traditional distributed systems. Indeed, the research on blockchain-based infrastructures tailored to genomics is on the rise. However, there is a lack of a comprehensive literature review that summarizes the current state-of-the-art methods in the applications of blockchain in genomics. In this paper, we systematically look at the existing work both commercial and academic, and discuss the major opportunities and challenges. Our study is driven by five research questions that we aim to answer in our review. We also present our projections of future research directions which we hope the researchers interested in the area can benefit from.

cs.CR↗

arXiv2021

KotlinDetector: Towards Understanding the Implications of Using Kotlin in Android Applications

Java programming language has been long used to develop native Android mobile applications. In the last few years many companies and freelancers have switched into using Kotlin partially or entirely. As such, many projects are released as binaries and employ a mix of Java and Kotlin language constructs. Yet, the true security and privacy implications of this shift have not been thoroughly studied. In this work, a state-of-the-art tool, KotlinDetector, is developed to directly extract any Kotlin presence, percentages, and numerous language features from Android Application Packages (APKs) by performing heuristic pattern scanning and invocation tracing. Our evaluation study shows that the tool is considerably efficient and accurate. We further provide a use case in which the output of the KotlinDetector is combined with the output of an existing vulnerability scanner tool called AndroBugs to infer any security and/or privacy implications.

cs.CR↗

arXiv2021

Privacy-preserving Logistic Regression with Secret Sharing

Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Research that collects and combines datasets from various data custodians and jurisdictions can excessively benefit from the increased statistical power to support their analyzing goals. However, combining data from these various sources creates significant privacy concerns that need to be addressed. In this paper, we proposed secret sharing-based privacy-preserving logistic regression protocols using the Newton-Raphson method. Our proposed approaches are based on secure Multi-Party Computation (MPC) with different security settings to analyze data owned by several data holders. We conducted experiments on both synthetic data and real-world datasets and compared the efficiency and accuracy of them with those of an ordinary logistic regression model. Experimental results demonstrate that the proposed protocols are highly efficient and accurate. This study introduces iterative algorithms to simplify the federated training a logistic regression model in a privacy-preserving manner. Our implementation results show that our improved method can handle large datasets used in securely training a logistic regression from multiple sources.

cs.LG↗