Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing

Mobile crowdsensing, which collects environmental information from mobile phone users, is growing in popularity. These data can be used by companies for marketing surveys or decision making. However, collecting sensing data from other users may violate their privacy. Moreover, the data aggregator and/or the participants of crowdsensing may be untrusted entities. Recent studies have proposed randomized response schemes for anonymized data collection. This kind of data collection can analyze the sensing data of users statistically without precise information about other users\u27 sensing results. However, traditional randomized response schemes and their extensions require a large number of samples to achieve proper estimation. In this paper, we propose a new anonymized data-collection scheme that can estimate data distributions more accurately. Using simulations with synthetic and real datasets, we prove that our proposed method can reduce the mean squared error and the JS divergence by more than 85% as compared with other existing studies

Robust security against cyber threats with variety of captchaGüvenlik kodu çeşitliliği ile siber tehditlere karşı güçlü güvenliğin sağlanması

Cyberspace also brings about cybercrime, which is evolving along with the rapid progress of technology and internet. Captchars are used as a layer of security to prevent these crimes. It is a security mechanism designed to distinguish whether an entry is made by the user when entering a system and is used for protection against malicious bot programs. For this reason, it is important that the introduction is done by human or bot software.In this study, a safer Captcha combination test was presented based on Captcha types and Captcha studies. The proposed approach basically consists of three steps. In the first step, the user is asked to test with a simple text-based Captcha to avoid the difficulty of captcha testing. The second stage, when the first stage test is unsuccessful, offers a more complicated captcha test with text and picture. In the third stage, different-based captcha are presented which are more complex than the first two stages and will force the user. This approach makes it easier to distinguish the bot with the user, and the bot program's algorithm can be challenged with the variety of captcha combinations created. Extended English summary is in the end of Full Text PDF (TURKISH) file.ÖzetSiber dünyada, teknoloji ve internetin hızla ilerlemesi beraberinde gelişmekte olan siber suçları da getirmektedir. Güvenlik kodlar (captcha) bu suçları engellemek amacıyla oluşturulan bir güvenlik katmanı olarak kullanılırlar. Bir sisteme giriş yapıldığında girişin kullanıcı tarafından yapılıp yapılmadığının ayırt edilebilmesi için tasarlanmış bir güvenlik mekanizması ve kötü niyetli bot programlarına karşı korunma amaçlı kullanılır. Bu nedenle girişin insan mı yoksa bot yazılımı tarafından mı yapıldığı önem arz etmektedir.Bu çalışmada, Güvenlik kod (captcha) türleri ve yapılan Güvenlik kod (captcha) çalışmaları baz alınarak daha güvenli bir Güvenlik kod (captcha)  kombinasyon testi sunulmuştur. Önerilen yaklaşım temelde üç aşamadan oluşmaktadır. İlk aşamada kullanıcının Güvenlik kod (captcha) ile imtihanını zorlaştırmamak için metin tabanlı basit Güvenlik kod (captcha) ile test edilmesi istenmektedir. İkinci aşamada, ilk aşama testi başarısız olduğunda metin ve resim tabanlı daha zorlaştırılmış Güvenlik kod (captcha) testi sunulmaktadır. Üçüncü aşamada ise ilk iki aşamadan daha karmaşık ve kullanıcıyı zorlayacak farklı tabanlı Güvenlik kodu (captcha)  sunulmaktadır. Bu yaklaşım ile kullanıcı ile bot ayırımı daha kolay yapılabilmekte ve oluşturulan Güvenlik kodu (captcha)  birleşim çeşitliliği ile bot programlarının algoritmasına meydan okunabilmektedir.

Quadri-dimensional approach for data analytics in mobile networks

The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

On Location Privacy in LTE Networks

The article of record as published may be found at http://dx.doi.org/10.1109/TIFS.2017.2656470Published in: IEEE Transactions on Information Forensics and Security (Volume: 12 , Issue: 6 , June 2017)Location privacy is an ever increasing concern as the pervasiveness of computing becomes more ubiquitous. This is especially apparent at the intersection of privacy, convenience, and quality of service in cellular networks. In this paper, we show the long term evolution (LTE) signaling plane to be vulnerable to location-based attacks via the timing advance (TA) parameter. To this end, we adapt the Cramér-Rao lower bound for timing advance-based estimation and show the associated estimator to be efficient. The analysis is complemented with numerical studies that feature synthetic and real-world data collected in existing LTE network deployments. Additionally, the Cellular Synchronization Assisted Refinement algorithm, a method of TA-based attack augmentation is examined. We show how it can simultaneously improve location resolution and negate the effects of poor network infrastructure geometry. The analysis and simulation demonstrate that a localization attack can yield resolution as high as 40 m