Akgün, Mete
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Akgün, M.
Akguen, Mete
Akgun, M.
Akgun, Mete
Akguen, Mete
Akgun, M.
Akgun, Mete
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meteakgun@iyte.edu.tr
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03.04. Department of Computer Engineering
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Documents
46
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41
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516
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2
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31
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49
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4.43
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7.00
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6
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2
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Master Thesis Privacy-Preserving Rare Disease Analysis With Fully Homomorphic Encryption(01. Izmir Institute of Technology, 2023) Akkaya, Güliz; Erdoğmuş, Nesli; Akgün, MeteRare diseases severely affect many people across the world at the present time. Researchers conduct studies to understand the reasons behind rare diseases and as a result of this research, diagnosis, and treatment methods are developed. Rare disease analysis is performed to specify the disease-causing variants on the genome data of patients. The researchers need access to as much genome data as possible to find causing variants of rare diseases. On the other hand, the genome data of patients should be protected because it can be used to detect the identity of individuals. The researchers are not able to share the genome data of patients easily because of regulations such as General Data Protection Regulation (GDPR). For this reason, rare disease analysis should be performed in a secure way that protects the privacy of patients while enabling the collaboration of multiple medical institutions. In this context, a privacy-preserving collaborative system for rare disease analysis should be provided. This thesis study focuses on the utilization of fully homomorphic encryption, a method that enables unlimited number of operations to be performed on encrypted data, for privacy-preserving collaborative rare disease analysis. Two different methods, the boolean circuit method, and the integer arithmetic method, are implemented to perform rare disease analysis on the encrypted genome data to find disease-causing variants, and various experiments are performed to assess the efficiency of the proposed methods.Article Citation - WoS: 1Citation - Scopus: 1Prism: Privacy-Preserving Rare Disease Analysis Using Fully Homomorphic Encryption(Oxford Univ Press, 2025) Akkaya, Guliz; Erdogmus, Nesli; Akgun, MeteMotivation Rare diseases affect millions of people worldwide, yet their genomic foundations remain poorly understood due to limited patient data and strict privacy regulations, such as the General Data Protection Regulation (GDPR) (https://gdpr.eu/tag/gdpr/) in March 2025. These restrictions can hinder the collaborative analysis of genomic data necessary for uncovering disease-causing variants.Results We present PRISM, a novel privacy-preserving framework based on fully homomorphic encryption (FHE) that facilitates rare disease variant analysis across multiple institutions without exposing sensitive genomic information. To address the challenges of centralized trust, PRISM is built upon a Threshold FHE scheme. This approach decentralizes key management across participating institutions and ensures no single entity can unilaterally decrypt sensitive data. Our method filters disease-causing variants under recessive, dominant, and de novo inheritance models entirely on encrypted data. We propose two algorithmic variants: a multiplication-intensive (MUL-IN) approach and an addition-intensive (ADD-IN) approach. The ADD-IN algorithms minimize the number of costly multiplication operations, enabling up to a 17x improvement in runtime for recessive/dominant filtering and 22x for de novo filtering, compared to MUL-IN methods. While ADD-IN produces larger ciphertexts, efficient parallelization via SIMD and multithreading allows it to handle millions of variants in reasonable time. To the best of our knowledge, this is the first study that utilizes FHE for privacy-preserving rare disease analysis across multiple inheritance models, demonstrating its practicality and scalability in a single-cloud setting.Availability and implementation The source code and the data used in this work can be found in https://github.com/mdppml/PRISM.git.Article Citation - WoS: 6Citation - Scopus: 10Efficient Privacy-Preserving Whole-Genome Variant Queries(Oxford University Press, 2022) Akgün, Mete; Pfeifer, Nico; Kohlbacher, OliverMotivation: Diagnosis and treatment decisions on genomic data have become widespread as the cost of genome sequencing decreases gradually. In this context, disease-gene association studies are of great importance. However, genomic data are very sensitive when compared to other data types and contains information about individuals and their relatives. Many studies have shown that this information can be obtained from the query-response pairs on genomic databases. In this work, we propose a method that uses secure multi-party computation to query genomic databases in a privacy-protected manner. The proposed solution privately outsources genomic data from arbitrarily many sources to the two non-colluding proxies and allows genomic databases to be safely stored in semi-honest cloud environments. It provides data privacy, query privacy and output privacy by using XOR-based sharing and unlike previous solutions, it allows queries to run efficiently on hundreds of thousands of genomic data. Results: We measure the performance of our solution with parameters similar to real-world applications. It is possible to query a genomic database with 3 000 000 variants with five genomic query predicates under 400 ms. Querying 1 048 576 genomes, each containing 1 000 000 variants, for the presence of five different query variants can be achieved approximately in 6 min with a small amount of dedicated hardware and connectivity. These execution times are in the right range to enable real-world applications in medical research and healthcare. Unlike previous studies, it is possible to query multiple databases with response times fast enough for practical application. To the best of our knowledge, this is the first solution that provides this performance for querying large-scale genomic data.Master Thesis P/Key: Puf Based Second Factor Authentication(01. Izmir Institute of Technology, 2022) Uysal, Ertan; Akgün, Mete; Şahin, SerapSecond-factor authentication mechanisms increase the security of authentication processes by implementing an additional auxiliary layer to a single factor. As a second factor, using one-time passwords (OTP) is mainly preferred due to their hardware independence and easy generation. OTP generation protocols should be evaluated in two main categories: time and security. In time-based OTP mechanisms (TOTP), client and server store a shared secret key. However, if attackers compromise the server, attackers can generate new OTPs using the key and impersonate the client. To solve this problem, protocols based on the hash chain mechanism have been proposed; however, these methods have weaknesses mainly due to the authentication speed and the limited number of OTPs they generate. This thesis proposes a server-side tamper-proof and fast response physical unclonable function (PUF) based second-factor authentication protocol on overcoming these problems. PUF is a digital fingerprint that ensures that every device produced is unique due to uncontrollable factors in the production stages of devices. It generates responses that correspond to challenges. Since PUF is based on the micro-level differences in devices, micro-level structure changes in the event of an attack, and the PUF takes to generate different responses. Although PUF is a fast response function, it is impossible to reach the challenge from the response it generates. In the proposed protocol, the PUF inside the server generates key values and used to store clients’ secret seed values securely. In case of side-channel attack on server-side, the key values of the clients cannot be obtained by the attackers, as the PUF structure will be corrupted. Even if the attacker obtains the server’s credentials and gains access to the system, they cannot get the secret seed values of the clients and cannot generate the OTPs. In this way, the attacker cannot authenticate by impersonating the client.Article Citation - WoS: 3Citation - Scopus: 4Scalable Rfid Authentication Protocol Based on Physically Unclonable Functions(Elsevier, 2023) Kurt, Işıl; Alagöz, Fatih; Akgün, MeteRadio Frequency Identification (RFID) technology is commonly used for tracking and identifying objects. However, this technology poses serious security and privacy concerns for individuals carrying the tags. To address these issues, various security protocols have been proposed. Unfortunately, many of these solutions suffer from scalability problems, requiring the back-end server to work linearly in the number of tags for a single tag identification. Some protocols offer O(1) or O(log n) identification complexity but are still susceptible to serious attacks. Few protocols consider attacks on the reader-side. Our proposed RFID authentication protocol eliminates the need for a search in the back-end and leverages Physically Unclonable Functions (PUFs) to securely store tag secrets, making it resistant to tag corruption attacks. It provides constant-time identification without sacrificing privacy and offers log2 n times better identification performance than the state-of-the-art protocol. It ensures destructive privacy for tag holders in the event of reader corruption without any conditions. Furthermore, it enables offline readers to maintain destructive privacy in case of corruption.Article Citation - WoS: 16Citation - Scopus: 25A Privacy-Preserving Scheme for Smart Grid Using Trusted Execution Environment(IEEE, 2023) Akgün, Mete; Üstündağ Soykan, Elif; Soykan, GürkanThe increasing transformation from the legacy power grid to the smart grid brings new opportunities and challenges to power system operations. Bidirectional communications between home-area devices and the distribution system empower smart grid functionalities. More granular energy consumption data flows through the grid and enables better smart grid applications. This may also lead to privacy violations since the data can be used to infer the consumer's residential behavior, so-called power signature. Energy utilities mostly aggregate the data, especially if the data is shared with stakeholders for the management of market operations. Although this is a privacy-friendly approach, recent works show that this does not fully protect privacy. On the other hand, some applications, like nonintrusive load monitoring, require disaggregated data. Hence, the challenging problem is to find an efficient way to facilitate smart grid operations without sacrificing privacy. In this paper, we propose a privacy-preserving scheme that leverages consumer privacy without reducing accuracy for smart grid applications like load monitoring. In the proposed scheme, we use a trusted execution environment (TEE) to protect the privacy of the data collected from smart appliances (SAs). The scheme allows customer-oriented smart grid applications as the scheme does not use regular aggregation methods but instead uses customer-oriented aggregation to provide privacy. Hence the accuracy loss stemming from disaggregation is prevented. Our scheme protects the transferred consumption data all the way from SAs to Utility so that possible false data injection attacks on the smart meter that aims to deceive the energy request from the grid are also prevented. We conduct security and game-based privacy analysis under the threat model and provide performance analysis of our implementation. Our results demonstrate that the proposed method overperforms other privacy methods in terms of communication and computation cost. The execution time of aggregation for 10,000 customers, each has 20 SAs is approximately 1 second. The decryption operations performed on the TEE have a linear complexity e.g., 172800 operations take around 1 second while 1728000 operations take around 10 seconds. These results can scale up using cloud or hyper-scalers for real-world applications as our scheme performs offline aggregation.Article Citation - WoS: 5Citation - Scopus: 9P/Key: Puf Based Second Factor Authentication(Public Library of Science, 2023) Uysal, Ertan; Akgün, MeteOne-time password (OTP) mechanisms are widely used to strengthen authentication processes. In time-based one-time password (TOTP) mechanisms, the client and server store common secrets. However, once the server is compromised, the client’s secrets are easy to obtain. To solve this issue, hash-chain-based second-factor authentication protocols have been proposed. However, these protocols suffer from latency in the generation of OTPs on the client side because of the hash-chain traversal. Secondly, they can generate only a limited number of OTPs as it depends on the length of the hash-chain. In this paper, we propose a second-factor authentication protocol that utilizes Physically Unclonable Functions (PUFs) to overcome these problems. In the proposed protocol, PUFs are used to store the secrets of the clients securely on the server. In case of server compromise, the attacker cannot obtain the seeds of clients’ secrets and can not generate valid OTPs to impersonate the clients. In the case of physical attacks, including side-channel attacks on the server side, our protocol has a mechanism that prevents attackers from learning the secrets of a client interacting with the server. Furthermore, our protocol does not incur any client-side delay in OTP generation.