arXiv subjects

S. Camtepe

Publications and source records attributed to S. Camtepe.

8 recordsLinked to original sources

arXiv2026

Quantum Spectral Authentication: Entity Authentication and Key Derivation from a Hidden Eigenstate of a Public Unitary Challenge

We introduce Quantum Spectral Authentication (QSA), a symmetric-key entity-authentication and key-derivation protocol in which a remote endpoint proves it still holds a hidden planted state, an eigenstate of the challenge it can prepare, without revealing it. Each round issues a fresh public unitary challenge with its own planted state, and the endpoint returns an eigenphase feature from which both parties derive transcript-bound session material. QSA runs in two provisioning modes with materially different security: a seed mode, storing a classical seed that regenerates the planted state, and a state mode, holding only the physical register carrying it, non-copyable and consumed on use. We give a formal security model in which QSA is an identification scheme, prove mutual authentication under a single label-hiding assumption, and prove unconditionally that the published spectrum is near-uniform at the readout resolution, leaving at least $m-1$ bits of min-entropy per instance; state mode satisfies the assumption by construction. We develop a symmetric verifier-driven compiler compatible with low-depth quantum phase estimation, which also admits a fast path that recovers the label by an inverse-compiler measurement. Simulations show it is more noise-tolerant than an asymmetric alternative, and experiments on IBM ibm_fez provide a hardware sanity check.

quant-ph↗

arXiv2025

Unlocking the hidden potential of pulsar astronomy

Pulsars have traditionally been used for research into fundamental physics and astronomy. In this paper, we investigate the expanding applications of radio pulsars in societal and industrial domains beyond their conventional scientific roles. We describe emerging applications in positioning, navigation, timing and synchronization, random number generation, space weather monitoring, public engagement, antenna calibration techniques, and leveraging extensive pulsar data sets generated by large-scale observatories. Such pulsar data sets have already been used to demonstrate quantum-computing algorithms. We evaluate the potential for compact radio receiver systems for pulsar detection by describing optimal observing bands. We show that relatively simple and compact receiver systems can detect the brightest pulsar, Vela. The equivalent of an ~4m-diameter dish with a small bandwidth operating around 700 MHz would be able to detect many more pulsars. Such a detector would be able to localise itself to around 10 km using pulsar navigation techniques. The space weather community requires direct measurements of the integrated electron density at a range of solar elongations. The only method to get model-independent values is through pulsar observations and we explore the possibility of measuring dispersion measures (DMs) and rotation measures with a range of telescopes (observing from low to mid-frequencies) as well as using a typical model to predict the variation of the DM as a function of solar radii. We review how pulsars can be used to produce random sequences and demonstrate that such sequences can be produced using the scintillation properties of pulsars as well as from pulse jitter.

astro-ph.IM↗

arXiv2021

Privacy Preserving Distributed Machine Learning with Federated Learning

Edge computing and distributed machine learning have advanced to a level that can revolutionize a particular organization. Distributed devices such as the Internet of Things (IoT) often produce a large amount of data, eventually resulting in big data that can be vital in uncovering hidden patterns, and other insights in numerous fields such as healthcare, banking, and policing. Data related to areas such as healthcare and banking can contain potentially sensitive data that can become public if they are not appropriately sanitized. Federated learning (FedML) is a recently developed distributed machine learning (DML) approach that tries to preserve privacy by bringing the learning of an ML model to data owners'. However, literature shows different attack methods such as membership inference that exploit the vulnerabilities of ML models as well as the coordinating servers to retrieve private data. Hence, FedML needs additional measures to guarantee data privacy. Furthermore, big data often requires more resources than available in a standard computer. This paper addresses these issues by proposing a distributed perturbation algorithm named as DISTPAB, for privacy preservation of horizontally partitioned data. DISTPAB alleviates computational bottlenecks by distributing the task of privacy preservation utilizing the asymmetry of resources of a distributed environment, which can have resource-constrained devices as well as high-performance computers. Experiments show that DISTPAB provides high accuracy, high efficiency, high scalability, and high attack resistance. Further experiments on privacy-preserving FedML show that DISTPAB is an excellent solution to stop privacy leaks in DML while preserving high data utility.

cs.DB↗

arXiv2020

Privacy Preserving Face Recognition Utilizing Differential Privacy

Facial recognition technologies are implemented in many areas, including but not limited to, citizen surveillance, crime control, activity monitoring, and facial expression evaluation. However, processing biometric information is a resource-intensive task that often involves third-party servers, which can be accessed by adversaries with malicious intent. Biometric information delivered to untrusted third-party servers in an uncontrolled manner can be considered a significant privacy leak (i.e. uncontrolled information release) as biometrics can be correlated with sensitive data such as healthcare or financial records. In this paper, we propose a privacy-preserving technique for "controlled information release", where we disguise an original face image and prevent leakage of the biometric features while identifying a person. We introduce a new privacy-preserving face recognition protocol named PEEP (Privacy using EigEnface Perturbation) that utilizes local differential privacy. PEEP applies perturbation to Eigenfaces utilizing differential privacy and stores only the perturbed data in the third-party servers to run a standard Eigenface recognition algorithm. As a result, the trained model will not be vulnerable to privacy attacks such as membership inference and model memorization attacks. Our experiments show that PEEP exhibits a classification accuracy of around 70% - 90% under standard privacy settings.

cs.CR↗

arXiv2019

Local Differential Privacy for Deep Learning

The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations such as the amalgamation of software-defined networks (SDN) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is becoming popular due to its remarkable accuracy when trained with a massive amount of data, such as generated by IoT. However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data. Existing privacy-preserving DL algorithms rely on the traditional server-centric approaches requiring high processing powers. We propose a new local differentially private (LDP) algorithm named LATENT that redesigns the training process. LATENT enables a data owner to add a randomization layer before data leave the data owners' devices and reach a potentially untrusted machine learning service. This feature is achieved by splitting the architecture of a convolutional neural network (CNN) into three layers: (1) convolutional module, (2) randomization module, and (3) fully connected module. Hence, the randomization module can operate as an NFV privacy preservation service in an SDN-controlled NFV, making LATENT more practical for IoT-driven cloud-based environments compared to existing approaches. The randomization module employs a newly proposed LDP protocol named utility enhancing randomization, which allows LATENT to maintain high utility compared to existing LDP protocols. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g. 91%- 96%) with high model quality even under low privacy budgets (e.g. $\varepsilon=0.5$).

cs.LG↗

arXiv2019

An Efficient and Scalable Privacy Preserving Algorithm for Big Data and Data Streams

A vast amount of valuable data is produced and is becoming available for analysis as a result of advancements in smart cyber-physical systems. The data comes from various sources, such as healthcare, smart homes, smart vehicles, and often includes private, potentially sensitive information that needs appropriate sanitization before being released for analysis. The incremental and fast nature of data generation in these systems necessitates scalable privacy-preserving mechanisms with high privacy and utility. However, privacy preservation often comes at the expense of data utility. We propose a new data perturbation algorithm, SEAL (Secure and Efficient data perturbation Algorithm utilizing Local differential privacy), based on Chebyshev interpolation and Laplacian noise, which provides a good balance between privacy and utility with high efficiency and scalability. Empirical comparisons with existing privacy-preserving algorithms show that SEAL excels in execution speed, scalability, accuracy, and attack resistance. SEAL provides flexibility in choosing the best possible privacy parameters, such as the amount of added noise, which can be tailored to the domain and dataset.

cs.CR↗

arXiv2019

Efficient privacy preservation of big data for accurate data mining

Computing technologies pervade physical spaces and human lives, and produce a vast amount of data that is available for analysis. However, there is a growing concern that potentially sensitive data may become public if the collected data are not appropriately sanitized before being released for investigation. Although there are more than a few privacy-preserving methods available, they are not efficient, scalable or have problems with data utility, and/or privacy. This paper addresses these issues by proposing an efficient and scalable nonreversible perturbation algorithm, PABIDOT, for privacy preservation of big data via optimal geometric transformations. PABIDOT was tested for efficiency, scalability, resistance, and accuracy using nine datasets and five classification algorithms. Experiments show that PABIDOT excels in execution speed, scalability, attack resistance and accuracy in large-scale privacy-preserving data classification when compared with two other, related privacy-preserving algorithms.

cs.DB↗

arXiv2018

Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining

The widespread use of the Internet of Things (IoT) has raised many concerns, including the protection of private information. Existing privacy preservation methods cannot provide a good balance between data utility and privacy, and also have problems with efficiency and scalability. This paper proposes an efficient data stream perturbation method (named as $P^2RoCAl$). $P^2RoCAl$ offers better data utility than similar methods: classification accuracies of $P^2RoCAl$ perturbed data streams are very close to those of the original data streams. $P^2RoCAl$ also provides higher resilience against data reconstruction attacks.

cs.DB↗