Ali Inan - Academia.edu (original) (raw)

Papers by Ali Inan

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

Differential privacy (DP) has emerged as a popular standard for privacy protection and received g... more Differential privacy (DP) has emerged as a popular standard for privacy protection and received great attention from the research community. However, practitioners often find DP cumbersome to implement, since it requires additional protocols (e.g., for randomized response, noise addition) and changes to existing database systems. To avoid these issues we introduce Explode, a platform for differentially private data analysis. The power of Explode comes from its ease of deployment and use: The data owner can install Explode on top of an SQL server, without modifying any existing components. Explode then hosts a web application that allows users to conveniently perform many popular data analysis tasks through a graphical user interface, e.g., issuing statistical queries, classification, correlation analysis. Explode automatically converts these tasks to collections of SQL queries, and uses the techniques in [3] to determine the right amount of noise that should be added to satisfy DP while producing high utility outputs. This paper describes the current implementation of Explode, together with potential improvements and extensions.

INTERNATIONAL JOURNAL OF INFORMATICS TECHNOLOGIES, 2017

Mahremiyet temel bir insan hakkidir ve 2010 tarihinde yapilan Anayasa degisikligiyle Turkiye Cumh... more Mahremiyet temel bir insan hakkidir ve 2010 tarihinde yapilan Anayasa degisikligiyle Turkiye Cumhuriyeti vatandaslari icin guvence altina alinmistir. Mahremiyet, 7 Nisan 2016 tarihinde Resmi Gazete’de yayinlanan Kisisel Verilerin Korunmasi Kanunu ile korunmaktadir. Bu kanun ile beraber hem ozel sektor hem de kamu kurumlarinda veri korumasi konusunun tartismaya acilmasi beklenmektedir. Ote yandan ulkemizce egitim alaninda e-Okul ile baslayip FATIH projesi ile cok daha kapsamli hale gelen dijital donusum projeleri halen devam etmektedir. Avrupa Birligi’nde bu tarz buyuk capli projelere baslanmadan once mahremiyet etki degerlendirmesi yapilmasi zorunludur. Projelerin tasarim ve uygulanma asamasi ise bu etki degerlendirmeleri dikkate alinarak gerceklestirilmektedir. Bu makalenin amaci FATIH projesi goz onune alinarak ogrenci verilerinin korunmasi ile ilgili teknik hususlarin degerlendirilmesidir. Bu bakimdan Turkiye’de yazarlarin bilgisi dahilinde ilk kez yapilan bu calismanin ileride f...

IEEE Transactions on Dependable and Secure Computing, 2017

Differential privacy (DP) has gained significant attention lately as the state of the art in priv... more Differential privacy (DP) has gained significant attention lately as the state of the art in privacy protection. It achieves privacy by adding noise to query answers. We study the problem of privately and accurately answering a set of statistical range queries in batch mode (i.e., under non-interactive DP). The noise magnitude in DP depends directly on the sensitivity of a query set, and calculating sensitivity was proven to be NP-hard. Therefore, efficiently bounding the sensitivity of a given query set is still an open research problem. In this work, we propose upper bounds on sensitivity that are tighter than those in previous work. We also propose a formulation to exactly calculate sensitivity for a set of COUNT queries. However, it is impractical to implement these bounds without sophisticated methods. We therefore introduce methods that build a graph model G based on a query set Q, such that implementing the aforementioned bounds can be achieved by solving two well-known clique problems on G. We make use of the literature in solving these clique problems to realize our bounds efficiently. Experimental results show that for query sets with a few hundred queries, it takes only a few seconds to obtain results.

Data Mining and Knowledge Discovery, 2017

Instance-based learning, and the k-nearest neighbors algorithm (k-NN) in particular, provide simp... more Instance-based learning, and the k-nearest neighbors algorithm (k-NN) in particular, provide simple yet effective classification algorithms for data mining. Classifiers are often executed on sensitive information such as medical or personal data. Differential privacy has recently emerged as the accepted standard for privacy protection in sensitive data. However, straightforward applications of differential privacy to k-NN classification yield rather inaccurate results. Motivated by this, we develop algorithms to increase the accuracy of private instance-based classification. We first describe the radius neighbors classifier (r-N) and show that its accuracy under differential privacy can be greatly improved by a non-trivial sensitivity analysis. Then, for k-NN classification, we build algorithms that convert k-NN classifiers to r-N

IEEE Transactions on Learning Technologies, 2017

Educational data contains valuable information that can be harvested through learning analytics t... more Educational data contains valuable information that can be harvested through learning analytics to provide new insights for a better education system. However, sharing or analysis of this data introduce privacy risks for the data subjects, mostly students. Existing work in the learning analytics literature identifies the need for privacy and pose interesting research directions, but fails to apply state of the art privacy protection methods with quantifiable and mathematically rigorous privacy guarantees. This work aims to employ and evaluate such methods on learning analytics by approaching the problem from two perspectives: (1) the data is anonymized and then shared with a learning analytics expert, and (2) the learning analytics expert is given a privacy-preserving interface that governs her access to the data. We develop proof-of-concept implementations of privacy preserving learning analytics tasks using both perspectives and run them on real and synthetic datasets. We also present an experimental study on the trade-off between individuals' privacy and the accuracy of the learning analytics tasks.

Fifth IEEE International Conference on Data Mining (ICDM'05)

Enterprises have been collecting data for many reasons including better customer relationship man... more Enterprises have been collecting data for many reasons including better customer relationship management, and high-level decision making. Public safety was another motivation for large-scale data collection efforts initiated by government agencies. However, such ...

Data & Knowledge Engineering, 2007

k-Anonymity is a method for providing privacy protection by ensuring that data cannot be traced t... more k-Anonymity is a method for providing privacy protection by ensuring that data cannot be traced to an individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. To achieve optimal and practical k-anonymity, recently, many different kinds of algorithms with various assumptions and restrictions have been proposed with different metrics to measure quality. This paper evaluates

Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2014

AbstractGears are used in the design of modern machines extensively. These gears are of various t... more AbstractGears are used in the design of modern machines extensively. These gears are of various types according to their tooth profiles and tooth widths, and can be manufactured in various sizes as small as the ones in small appliances to the very large gears used in heavy industry. In addition, some of the gears have proven benefits, and universally accepted standard manufacturing methods have been developed in time for these gears. However, researchers have always made more experiments, believing that there is no end in science, and have performed research activities in order to contribute to the science a little more. And numerous researches have been carried out on the gear wheels, and many successful studies have been presented. In this study, involute profile cylindrical gears were manufactured at the CNC milling machines and the results were evaluated. The idea that these involute profile cylindrical gears can be manufactured at the CNC milling machines by two different methods, which are completely different from the present manufacturing methods, was proposed by other researchers. Although these gears were proven useful by the researchers, they were not widespread because of the previous unsuccessful methods developed for their production.