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Erman Ayday

Publications and source records attributed to Erman Ayday.

At least 55 records · Page 3Linked to original sources
arXiv2024

Privacy-Preserving Optimal Parameter Selection for Collaborative Clustering

This study investigates the optimal selection of parameters for collaborative clustering while ensuring data privacy. We focus on key clustering algorithms within a collaborative framework, where multiple data owners combine their data. A semi-trusted server assists in recommending the most suitable clustering algorithm and its parameters. Our findings indicate that the privacy parameter ($ε$) minimally impacts the server's recommendations, but an increase in $ε$ raises the risk of membership inference attacks, where sensitive information might be inferred. To mitigate these risks, we implement differential privacy techniques, particularly the Randomized Response mechanism, to add noise and protect data privacy. Our approach demonstrates that high-quality clustering can be achieved while maintaining data confidentiality, as evidenced by metrics such as the Adjusted Rand Index and Silhouette Score. This study contributes to privacy-aware data sharing, optimal algorithm and parameter selection, and effective communication between data owners and the server.

cs.LG↗
arXiv2023

Robust Fingerprint of Location Trajectories Under Differential Privacy

Directly releasing those data raises privacy and liability (e.g., due to unauthorized distribution of such datasets) concerns since location data contain users' sensitive information, e.g., regular moving patterns and favorite spots. To address this, we propose a novel fingerprinting scheme that simultaneously identifies unauthorized redistribution of location datasets and provides differential privacy guarantees for the shared data. Observing data utility degradation due to differentially-private mechanisms, we introduce a utility-focused post-processing scheme to regain spatio-temporal correlations between points in a location trajectory. We further integrate this post-processing scheme into our fingerprinting scheme as a sampling method. The proposed fingerprinting scheme alleviates the degradation in the utility of the shared dataset due to the noise introduced by differentially-private mechanisms (i.e., adds the fingerprint by preserving the publicly known statistics of the data). Meanwhile, it does not violate differential privacy throughout the entire process due to immunity to post-processing, a fundamental property of differential privacy. Our proposed fingerprinting scheme is robust against known and well-studied attacks against a fingerprinting scheme including random flipping attacks, correlation-based flipping attacks, and collusions among multiple parties, which makes it hard for the attackers to infer the fingerprint codes and avoid accusation. Via experiments on two real-life location datasets and two synthetic ones, we show that our scheme achieves high fingerprinting robustness and outperforms existing approaches. Besides, the proposed fingerprinting scheme increases data utility for differentially-private datasets, which is beneficial for data analyzers.

cs.CR↗
arXiv2022

zkFaith: Soonami's Zero-Knowledge Identity Protocol

Individuals are encouraged to prove their eligibility to access specific services regularly. However, providing various organizations with personal data spreads sensitive information and endangers people's privacy. Hence, privacy-preserving identification systems that enable individuals to prove they are permitted to use specific services are required to fill the gap. Cryptographic techniques are deployed to construct identity proofs across the internet; nonetheless, they do not offer complete control over personal data or prevent users from forging and submitting fake data. In this paper, we design a privacy-preserving identity protocol called "zkFaith." A new approach to obtain a verified zero-knowledge identity unique to each individual. The protocol verifies the integrity of the documents provided by the individuals and issues a zero-knowledge-based id without revealing any information to the authenticator or verifier. The zkFaith leverages an aggregated version of the Camenisch-Lysyanskaya (CL) signature scheme to sign the user's commitment to the verified personal data. Then the users with a zero-knowledge proof system can prove that they own the required attributes of the access criterion of the requested service providers. Vector commitment and their position binding property enables us to, later on, update the commitments based on the modification of the personal data; hence update the issued zkFaith id with no requirement of initiating the protocol from scratch. We show that the design and implementation of the zkFaith with the generated proofs in real-world scenarios are scalable and comparable with the state-of-the-art schemes.

cs.CR↗
arXiv2022

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. Workflow systems model and record provenance describing the steps performed to obtain the final results of a computation. In this work, we propose a framework that verifies the correctness of the statistical test results that are conducted by a researcher while protecting individuals' privacy in the researcher's dataset. The researcher publishes the workflow of the conducted study, its output, and associated metadata. They keep the research dataset private while providing, as part of the metadata, a partial noisy dataset (that achieves local differential privacy). To check the correctness of the workflow output, a verifier makes use of the workflow, its metadata, and results of another statistical study (using publicly available datasets) to distinguish between correct statistics and incorrect ones. We use case the proposed framework in the genome-wide association studies (GWAS), in which the goal is to identify highly associated point mutations (variants) with a given phenotype. For evaluation, we use real genomic data and show that the correctness of the workflow output can be verified with high accuracy even when the aggregate statistics of a small number of variants are provided. We also quantify the privacy leakage due to the provided workflow and its associated metadata in the GWAS use-case and show that the additional privacy risk due to the provided metadata does not increase the existing privacy risk due to sharing of the research results. Thus, our results show that the workflow output (i.e., research results) can be verified with high confidence in a privacy-preserving way. We believe that this work will be a valuable step towards providing provenance in a privacy-preserving way while providing guarantees to the users about the correctness of the results.

cs.CR↗
arXiv2022

Privacy-Preserving Link Prediction

Consider two data holders, ABC and XYZ, with graph data (e.g., social networks, e-commerce, telecommunication, and bio-informatics). ABC can see that node A is linked to node B, and XYZ can see node B is linked to node C. Node B is the common neighbour of A and C but neither network can discover this fact on their own. In this paper, we provide a two party computation that ABC and XYZ can run to discover the common neighbours in the union of their graph data, however neither party has to reveal their plaintext graph to the other. Based on private set intersection, we implement our solution, provide measurements, and quantify partial leaks of privacy. We also propose a heavyweight solution that leaks zero information based on additively homomorphic encryption.

cs.CR↗
arXiv2022

ShareTrace: Contact Tracing with the Actor Model

Proximity-based contact tracing relies on mobile-device interaction to estimate the spread of disease. ShareTrace is one such approach that improves the efficacy of tracking disease spread by considering direct and indirect forms of contact. In this work, we utilize the actor model to provide an efficient and scalable formulation of ShareTrace with asynchronous, concurrent message passing on a temporal contact network. We also introduce message reachability, an extension of temporal reachability that accounts for network topology and message-passing semantics. Our evaluation on both synthetic and real-world contact networks indicates that correct parameter values optimize for algorithmic accuracy and efficiency. In addition, we demonstrate that message reachability can accurately estimate the risk a user poses to their contacts.

cs.DC↗
arXiv2022

Robust Fingerprinting of Genomic Databases

Database fingerprinting has been widely used to discourage unauthorized redistribution of data by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint by launching effective correlation attacks which leverage the intrinsic correlations among genomic data (e.g., Mendel's law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks. We first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g.,database accuracy and consistency of SNP-phenotype associations measured via p-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP-phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases.

cs.CR↗
arXiv2022

Facilitating Federated Genomic Data Analysis by Identifying Record Correlations while Ensuring Privacy

With the reduction of sequencing costs and the pervasiveness of computing devices, genomic data collection is continually growing. However, data collection is highly fragmented and the data is still siloed across different repositories. Analyzing all of this data would be transformative for genomics research. However, the data is sensitive, and therefore cannot be easily centralized. Furthermore, there may be correlations in the data, which if not detected, can impact the analysis. In this paper, we take the first step towards identifying correlated records across multiple data repositories in a privacy-preserving manner. The proposed framework, based on random shuffling, synthetic record generation, and local differential privacy, allows a trade-off of accuracy and computational efficiency. An extensive evaluation on real genomic data from the OpenSNP dataset shows that the proposed solution is efficient and effective.

cs.CR↗
arXiv2022

Privacy-Preserving Database Fingerprinting

When sharing sensitive relational databases with other parties, a database owner aims to (i) have privacy guarantees for the database entries, (ii) have liability guarantees (via fingerprinting) in case of unauthorized sharing of its database by the recipients, and (iii) provide a high quality (utility) database to the recipients. We observe that sharing a relational database with privacy and liability guarantees are orthogonal objectives. The former can be achieved by injecting noise into the database to prevent inference of the original data values, whereas, the latter can be achieved by hiding unique marks inside the database to trace malicious parties (data recipients) who redistribute the data without the authorization. We achieve these two objectives simultaneously by proposing a novel entry-level differentially-private fingerprinting mechanism for relational databases. At a high level, the proposed mechanism fulfills the privacy and liability requirements by leveraging the randomization nature that is intrinsic to fingerprinting and achieves desired entry-level privacy guarantees. To be more specific, we devise a bit-level random response scheme to achieve differential privacy guarantee for arbitrary data entries when sharing the entire database, and then, based on this, we develop an $ε$-entry-level differentially-private fingerprinting mechanism. Next, we theoretically analyze the relationships between privacy guarantee, fingerprint robustness, and database utility by deriving closed form expressions. The outcome of this analysis allows us to bound the privacy leakage caused by attribute inference attack and characterize the privacy-utility coupling and privacy-fingerprint robustness coupling. Furthermore, we also propose a SVT-based solution to control the cumulative privacy loss when fingerprinted copies of a database are shared with multiple recipients.

cs.CR↗
arXiv2021

GenShare: Sharing Accurate Differentially-Private Statistics for Genomic Datasets with Dependent Tuples

Motivation: Cutting the cost of DNA sequencing technology led to a quantum leap in the availability of genomic data. While sharing genomic data across researchers is an essential driver of advances in health and biomedical research, the sharing process is often infeasible due to data privacy concerns. Differential privacy is one of the rigorous mechanisms utilized to facilitate the sharing of aggregate statistics from genomic datasets without disclosing any private individual-level data. However, differential privacy can still divulge sensitive information about the dataset participants due to the correlation between dataset tuples. Results: Here, we propose GenShare model built upon Laplace-perturbation-mechanism-based DP to introduce a privacy-preserving query-answering sharing model for statistical genomic datasets that include dependency due to the inherent correlations between genomes of individuals (i.e., family ties). We demonstrate our privacy improvement over the state-of-the-art approaches for a range of practical queries including cohort discovery, minor allele frequency, and chi^2 association tests. With a fine-grained analysis of sensitivity in the Laplace perturbation mechanism and considering joint distributions, GenShare results near-achieve the formal privacy guarantees permitted by the theory of differential privacy as the queries that computed over independent tuples (only up to 6% differences). GenShare ensures that query results are as accurate as theoretically guaranteed by differential privacy. For empowering the advances in different scientific and medical research areas, GenShare presents a path toward an interactive genomic data sharing system when the datasets include participants with familial relationships.

q-bio.GN↗
arXiv2021

Privacy-Preserving Identification of Target Patients from Outsourced Patient Data

With the increasing affordability and availability of patient data, hospitals tend to outsource their data to cloud service providers (CSPs) for the purpose of storage and analytics. However, the concern of data privacy significantly limits the data owners' choice. In this work, we propose the first solution, to the best of our knowledge, that allows a CSP to perform efficient identification of target patients (e.g., pre-processing for a genome-wide association study - GWAS) over multi-tenant encrypted phenotype data (owned by multiple hospitals or data owners). We first propose an encryption mechanism for phenotype data, where each data owner is allowed to encrypt its data with a unique secret key. Moreover, the ciphertext supports privacy-preserving search and, consequently, enables the selection of the target group of patients (e.g., case and control groups). In addition, we provide a per-query based authorization mechanism for a client to access and operate on the data stored at the CSP. Based on the identified patients, the proposed scheme can either (i) directly conduct GWAS (i.e., computation of statistics about genomic variants) at the CSP or (ii) provide the identified groups to the client to directly query the corresponding data owners and conduct GWAS using existing distributed solutions. We implement the proposed scheme and run experiments over a real-life genomic dataset to show its effectiveness. The result shows that the proposed solution is capable to efficiently identify the case/control groups in a privacy-preserving way.

cs.CR↗
arXiv2021

The Curse of Correlations for Robust Fingerprinting of Relational Databases

Database fingerprinting have been widely adopted to prevent unauthorized sharing of data and identify the source of data leakages. Although existing schemes are robust against common attacks, like random bit flipping and subset attack, their robustness degrades significantly if attackers utilize the inherent correlations among database entries. In this paper, we first demonstrate the vulnerability of existing database fingerprinting schemes by identifying different correlation attacks: column-wise correlation attack, row-wise correlation attack, and the integration of them. To provide robust fingerprinting against the identified correlation attacks, we then develop mitigation techniques, which can work as post-processing steps for any off-the-shelf database fingerprinting schemes. The proposed mitigation techniques also preserve the utility of the fingerprinted database considering different utility metrics. We empirically investigate the impact of the identified correlation attacks and the performance of mitigation techniques using real-world relational databases. Our results show (i) high success rates of the identified correlation attacks against existing fingerprinting schemes (e.g., the integrated correlation attack can distort 64.8\% fingerprint bits by just modifying 14.2\% entries in a fingerprinted database), and (ii) high robustness of the proposed mitigation techniques (e.g., with the mitigation techniques, the integrated correlation attack can only distort $3\%$ fingerprint bits).

cs.CR↗
arXiv2021

Near-Optimal Privacy-Utility Tradeoff in Genomic Studies Using Selective SNP Hiding

Motivation: Researchers need a rich trove of genomic datasets that they can leverage to gain a better understanding of the genetic basis of the human genome and identify associations between phenotypes and specific parts of DNA. However, sharing genomic datasets that include sensitive genetic or medical information of individuals can lead to serious privacy-related consequences if data lands in the wrong hands. Restricting access to genomic datasets is one solution, but this greatly reduces their usefulness for research purposes. To allow sharing of genomic datasets while addressing these privacy concerns, several studies propose privacy-preserving mechanisms for data sharing. Differential privacy (DP) is one of such mechanisms that formalize rigorous mathematical foundations to provide privacy guarantees while sharing aggregated statistical information about a dataset. However, it has been shown that the original privacy guarantees of DP-based solutions degrade when there are dependent tuples in the dataset, which is a common scenario for genomic datasets (due to the existence of family members). Results: In this work, we introduce a near-optimal mechanism to mitigate the vulnerabilities of the inference attacks on differentially private query results from genomic datasets including dependent tuples. We propose a utility-maximizing and privacy-preserving approach for sharing statistics by hiding selective SNPs of the family members as they participate in a genomic dataset. By evaluating our mechanism on a real-world genomic dataset, we empirically demonstrate that our proposed mechanism can achieve up to 40% better privacy than state-of-the-art DP-based solutions, while near-optimally minimizing the utility loss.

cs.CR↗
arXiv2021

Genomic Data Sharing under Dependent Local Differential Privacy

Privacy-preserving genomic data sharing is prominent to increase the pace of genomic research, and hence to pave the way towards personalized genomic medicine. In this paper, we introduce ($ε, T$)-dependent local differential privacy (LDP) for privacy-preserving sharing of correlated data and propose a genomic data sharing mechanism under this privacy definition. We first show that the original definition of LDP is not suitable for genomic data sharing, and then we propose a new mechanism to share genomic data. The proposed mechanism considers the correlations in data during data sharing, eliminates statistically unlikely data values beforehand, and adjusts the probability distributions for each shared data point accordingly. By doing so, we show that we can avoid an attacker from inferring the correct values of the shared data points by utilizing the correlations in the data. By adjusting the probability distributions of the shared states of each data point, we also improve the utility of shared data for the data collector. Furthermore, we develop a greedy algorithm that strategically identifies the processing order of the shared data points with the aim of maximizing the utility of the shared data. Considering the interdependent privacy risks while sharing genomic data, we also analyze the information gain of an attacker about genomes of a donor's family members by observing perturbed data of the genome donor and we propose a mechanism to select the privacy budget (i.e., $ε$ parameter of LDP) of the donor by also considering privacy preferences of her family members. Our evaluation results on a real-life genomic dataset show the superiority of the proposed mechanism compared to the randomized response mechanism (a widely used technique to achieve LDP).

cs.CR↗
arXiv2020

Tracking the Invisible: Privacy-Preserving Contact Tracing to Control the Spread of a Virus

Today, tracking and controlling the spread of a virus is a crucial need for almost all countries. Doing this early would save millions of lives and help countries keep a stable economy. The easiest way to control the spread of a virus is to immediately inform the individuals who recently had close contact with the diagnosed patients. However, to achieve this, a centralized authority (e.g., a health authority) needs detailed location information from both healthy individuals and diagnosed patients. Thus, such an approach, although beneficial to control the spread of a virus, results in serious privacy concerns, and hence privacy-preserving solutions are required to solve this problem. Previous works on this topic either (i) compromise privacy (especially privacy of diagnosed patients) to have better efficiency or (ii) provide unscalable solutions. In this work, we propose a technique based on private set intersection between physical contact histories of individuals (that are recorded using smart phones) and a centralized database (run by a health authority) that keeps the identities of the positive diagnosed patients for the disease. Proposed solution protects the location privacy of both healthy individuals and diagnosed patients and it guarantees that the identities of the diagnosed patients remain hidden from other individuals. Notably, proposed scheme allows individuals to receive warning messages indicating their previous contacts with a positive diagnosed patient. Such warning messages will help them realize the risk and isolate themselves from other people. We make sure that the warning messages are only observed by the corresponding individuals and not by the health authority. We also implement the proposed scheme and show its efficiency and scalability via simulations.

cs.CR↗
arXiv2020

Efficient Quantification of Profile Matching Risk in Social Networks

Anonymous data sharing has been becoming more challenging in today's interconnected digital world, especially for individuals that have both anonymous and identified online activities. The most prominent example of such data sharing platforms today are online social networks (OSNs). Many individuals have multiple profiles in different OSNs, including anonymous and identified ones (depending on the nature of the OSN). Here, the privacy threat is profile matching: if an attacker links anonymous profiles of individuals to their real identities, it can obtain privacy-sensitive information which may have serious consequences, such as discrimination or blackmailing. Therefore, it is very important to quantify and show to the OSN users the extent of this privacy risk. Existing attempts to model profile matching in OSNs are inadequate and computationally inefficient for real-time risk quantification. Thus, in this work, we develop algorithms to efficiently model and quantify profile matching attacks in OSNs as a step towards real-time privacy risk quantification. For this, we model the profile matching problem using a graph and develop a belief propagation (BP)-based algorithm to solve this problem in a significantly more efficient and accurate way compared to the state-of-the-art. We evaluate the proposed framework on three real-life datasets (including data from four different social networks) and show how users' profiles in different OSNs can be matched efficiently and with high probability. We show that the proposed model generation has linear complexity in terms of number of user pairs, which is significantly more efficient than the state-of-the-art (which has cubic complexity). Furthermore, it provides comparable accuracy, precision, and recall compared to state-of-the-art.

cs.CR↗
arXiv2020

Genome Reconstruction Attacks Against Genomic Data-Sharing Beacons

Sharing genome data in a privacy-preserving way stands as a major bottleneck in front of the scientific progress promised by the big data era in genomics. A community-driven protocol named genomic data-sharing beacon protocol has been widely adopted for sharing genomic data. The system aims to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes/no queries on the presence of specific alleles in the dataset. However, beacon protocol was recently shown to be vulnerable against membership inference attacks. In this paper, we show that privacy threats against genomic data sharing beacons are not limited to membership inference. We identify and analyze a novel vulnerability of genomic data-sharing beacons: genome reconstruction. We show that it is possible to successfully reconstruct a substantial part of the genome of a victim when the attacker knows the victim has been added to the beacon in a recent update. We also show that even if multiple individuals are added to the beacon during the same update, it is possible to identify the victim's genome with high confidence using traits that are easily accessible by the attacker (e.g., eye and hair color). Moreover, we show how the reconstructed genome using a beacon that is not associated with a sensitive phenotype can be used for membership inference attacks to beacons with sensitive phenotypes (i.e., HIV+). The outcome of this work will guide beacon operators on when and how to update the content of the beacon. Thus, this work will be an important attempt at helping beacon operators and participants make informed decisions.

cs.CR↗
arXiv2020

Profile Matching Across Online Social Networks

In this work, we study the privacy risk due to profile matching across online social networks (OSNs), in which anonymous profiles of OSN users are matched to their real identities using auxiliary information about them. We consider different attributes that are publicly shared by users. Such attributes include both strong identifiers such as user name and weak identifiers such as interest or sentiment variation between different posts of a user in different platforms. We study the effect of using different combinations of these attributes to profile matching in order to show the privacy threat in an extensive way. The proposed framework mainly relies on machine learning techniques and optimization algorithms. We evaluate the proposed framework on three datasets (Twitter - Foursquare, Google+ - Twitter, and Flickr) and show how profiles of the users in different OSNs can be matched with high probability by using the publicly shared attributes and/or the underlying graphical structure of the OSNs. We also show that the proposed framework notably provides higher precision values compared to state-of-the-art that relies on machine learning techniques. We believe that this work will be a valuable step to build a tool for the OSN users to understand their privacy risks due to their public sharings.

cs.SI↗