arXiv subjects

Erman Ayday

Publications and source records attributed to Erman Ayday.

65 records · Page 4Linked to original sources

arXiv2020

Key Protected Classification for Collaborative Learning

Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving solution, by enabling training over a number of private datasets that are not shared by their owners. However, recently, it has been shown that the existing collaborative learning frameworks are vulnerable to an active adversary that runs a generative adversarial network (GAN) attack. In this work, we propose a novel classification model that is resilient against such attacks by design. More specifically, we introduce a key-based classification model and a principled training scheme that protects class scores by using class-specific private keys, which effectively hide the information necessary for a GAN attack. We additionally show how to utilize high dimensional keys to improve the robustness against attacks without increasing the model complexity. Our detailed experiments demonstrate the effectiveness of the proposed technique. Source code is available at https://github.com/mbsariyildiz/key-protected-classification.

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arXiv2020

Privacy-Preserving Search for a Similar Genomic Makeup in the Cloud

In this paper, we attempt to provide a privacy-preserving and efficient solution for the "similar patient search" problem among several parties (e.g., hospitals) by addressing the shortcomings of previous attempts. We consider a scenario in which each hospital has its own genomic dataset and the goal of a physician (or researcher) is to search for a patient similar to a given one (based on a genomic makeup) among all the hospitals in the system. To enable this search, we let each hospital encrypt its dataset with its own key and outsource the storage of its dataset to a public cloud. The physician can get authorization from multiple hospitals and send a query to the cloud, which efficiently performs the search across authorized hospitals using a privacy-preserving index structure. We propose a hierarchical index structure to index each hospital's dataset with low memory requirements. Furthermore, we develop a novel privacy-preserving index merging mechanism that generates a common search index from individual indices of each hospital to significantly improve the search efficiency. We also consider the storage of medical information associated with genomic data of a patient (e.g., diagnosis and treatment). We allow access to this information via a fine-grained access control policy that we develop through the combination of standard symmetric encryption and ciphertext policy attribute-based encryption. Using this mechanism, a physician can search for similar patients and obtain medical information about the matching records if the access policy holds. We conduct experiments on large-scale genomic data and show the efficiency of the proposed scheme. Notably, we show that under our experimental settings, the proposed scheme is more than $60$ times faster than Wang et al.'s protocol and $95$ times faster than Asharov et al.'s solution.

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arXiv2020

Collusion-Resilient Probabilistic Fingerprinting Scheme for Correlated Data

In order to receive personalized services, individuals share their personal data with a wide range of service providers, hoping that their data will remain confidential. Thus, in case of an unauthorized distribution of their personal data by these service providers (or in case of a data breach) data owners want to identify the source of such data leakage. Digital fingerprinting schemes have been developed to embed a hidden and unique fingerprint into shared digital content, especially multimedia, to provide such liability guarantees. However, existing techniques utilize the high redundancy in the content, which is typically not included in personal data. In this work, we propose a probabilistic fingerprinting scheme that efficiently generates the fingerprint by considering a fingerprinting probability (to keep the data utility high) and publicly known inherent correlations between data points. To improve the robustness of the proposed scheme against colluding malicious service providers, we also utilize the Boneh-Shaw fingerprinting codes as a part of the proposed scheme. Furthermore, observing similarities between privacy-preserving data sharing techniques (that add controlled noise to the shared data) and the proposed fingerprinting scheme, we make a first attempt to develop a data sharing scheme that provides both privacy and fingerprint robustness at the same time. We experimentally show that fingerprint robustness and privacy have conflicting objectives and we propose a hybrid approach to control such a trade-off with a design parameter. Using the proposed hybrid approach, we show that individuals can improve their level of privacy by slightly compromising from the fingerprint robustness. We implement and evaluate the performance of the proposed scheme on real genomic data. Our experimental results show the efficiency and robustness of the proposed scheme.

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arXiv2019

One-Time Programs made Practical

A one-time program (OTP) works as follows: Alice provides Bob with the implementation of some function. Bob can have the function evaluated exclusively on a single input of his choosing. Once executed, the program will fail to evaluate on any other input. State-of-the-art one-time programs have remained theoretical, requiring custom hardware that is cost-ineffective/unavailable, or confined to adhoc/unrealistic assumptions. To bridge this gap, we explore how the Trusted Execution Environment (TEE) of modern CPUs can realize the OTP functionality. Specifically, we build two flavours of such a system: in the first, the TEE directly enforces the one-timeness of the program; in the second, the program is represented with a garbled circuit and the TEE ensures Bob's input can only be wired into the circuit once, equivalent to a smaller cryptographic primitive called one-time memory. These have different performance profiles: the first is best when Alice's input is small and Bob's is large, and the second for the converse.

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arXiv2018

Privacy-Preserving Aggregate Queries for Optimal Location Selection

Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot share their data with other businesses, mainly due to privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be answered by data owners without sharing their data with other businesses and without accessing sensitive information such as the customer list of the businesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We prove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposed protocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show that the proposed solutions are highly practical. The proposed solutions will facilitate an effective sharing of sensitive data between entities and joint analytics in a wide range of applications without violating their customers' privacy.

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arXiv2017

Profile Matching Across Unstructured Online Social Networks: Threats and Countermeasures

In this work, we propose a profile matching (or deanonymization) attack for unstructured online social networks (OSNs) in which similarity in graphical structure cannot be used for profile matching. We consider different attributes that are publicly shared by users. Such attributes include both obvious identifiers such as the user name and non-obvious identifiers such as interest similarity or sentiment variation between different posts of a user in different platforms. We study the effect of using different combinations of these attributes to the profile matching in order to show the privacy threat in an extensive way. Our proposed framework mainly relies on machine learning techniques and optimization algorithms. We evaluate the proposed framework on two real-life datasets that are constructed by us. Our results indicate that profiles of the users in different OSNs can be matched with high probability by only using publicly shared attributes and without using the underlying graphical structure of the OSNs. We also propose possible countermeasures to mitigate this threat in the expense of reduction in the accuracy (or utility) of the attributes shared by the users. We formulate the tradeoff between the privacy and profile utility of the users as an optimization problem and show how slight changes in the profiles of the users would reduce the success of the attack. We believe that this work will be a valuable step to build a privacy-preserving tool for users against profile matching attacks between OSNs.

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arXiv2017

Collusion-Secure Watermarking for Sequential Data

In this work, we address the liability issues that may arise due to unauthorized sharing of personal data. We consider a scenario in which an individual shares his sequential data (such as genomic data or location patterns) with several service providers (SPs). In such a scenario, if his data is shared with other third parties without his consent, the individual wants to determine the service provider that is responsible for this unauthorized sharing. To provide this functionality, we propose a novel optimization-based watermarking scheme for sharing of sequential data. Thus, in the case of an unauthorized sharing of sensitive data, the proposed scheme can find the source of the leakage by checking the watermark inside the leaked data. In particular, the proposed schemes guarantees with a high probability that (i) the malicious SP that receives the data cannot understand the watermarked data points, (ii) when more than one malicious SPs aggregate their data, they still cannot determine the watermarked data points, (iii) even if the unauthorized sharing involves only a portion of the original data or modified data (to damage the watermark), the corresponding malicious SP can be kept responsible for the leakage, and (iv) the added watermark is compliant with the nature of the corresponding data. That is, if there are inherent correlations in the data, the added watermark still preserves such correlations. Watermarking typically means changing certain parts of the data, and hence it may have negative effects on data utility. The proposed scheme also minimizes such utility loss while it provides the aforementioned security guarantees. Furthermore, we conduct a case study of the proposed scheme on genomic data and show the security and utility guarantees of the proposed scheme.

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arXiv2016

Privacy-Related Consequences of Turkish Citizen Database Leak

Personal data is collected and stored more than ever by the governments and companies in the digital age. Even though the data is only released after anonymization, deanonymization is possible by joining different datasets. This puts the privacy of individuals in jeopardy. Furthermore, data leaks can unveil personal identifiers of individuals when security is breached. Processing the leaked dataset can provide even more information than what is visible to naked eye. In this work, we report the results of our analyses on the recent "Turkish citizen database leak", which revealed the national identifier numbers of close to fifty million voters, along with personal information such as date of birth, birth place, and full address. We show that with automated processing of the data, one can uniquely identify (i) mother's maiden name of individuals and (ii) landline numbers, for a significant portion of people. This is a serious privacy and security threat because (i) identity theft risk is now higher, and (ii) scammers are able to access more information about individuals. The only and utmost goal of this work is to point out to the security risks and suggest stricter measures to related companies and agencies to protect the security and privacy of individuals.

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arXiv2015

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward.

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arXiv2015

The Chills and Thrills of Whole Genome Sequencing

In recent years, Whole Genome Sequencing (WGS) evolved from a futuristic-sounding research project to an increasingly affordable technology for determining complete genome sequences of complex organisms, including humans. This prompts a wide range of revolutionary applications, as WGS promises to improve modern healthcare and provide a better understanding of the human genome -- in particular, its relation to diseases and response to treatments. However, this progress raises worrisome privacy and ethical issues, since, besides uniquely identifying its owner, the genome contains a treasure trove of highly personal and sensitive information. In this article, after summarizing recent advances in genomics, we discuss some important privacy issues associated with human genomic information and identify a number of particularly relevant research challenges.

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arXiv2012

BPRS: Belief Propagation Based Iterative Recommender System

In this paper we introduce the first application of the Belief Propagation (BP) algorithm in the design of recommender systems. We formulate the recommendation problem as an inference problem and aim to compute the marginal probability distributions of the variables which represent the ratings to be predicted. However, computing these marginal probability functions is computationally prohibitive for large-scale systems. Therefore, we utilize the BP algorithm to efficiently compute these functions. Recommendations for each active user are then iteratively computed by probabilistic message passing. As opposed to the previous recommender algorithms, BPRS does not require solving the recommendation problem for all the users if it wishes to update the recommendations for only a single active. Further, BPRS computes the recommendations for each user with linear complexity and without requiring a training period. Via computer simulations (using the 100K MovieLens dataset), we verify that BPRS iteratively reduces the error in the predicted ratings of the users until it converges. Finally, we confirm that BPRS is comparable to the state of art methods such as Correlation-based neighborhood model (CorNgbr) and Singular Value Decomposition (SVD) in terms of rating and precision accuracy. Therefore, we believe that the BP-based recommendation algorithm is a new promising approach which offers a significant advantage on scalability while providing competitive accuracy for the recommender systems.

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