Privacy Preserving Cryptographic Protocols for Secure Heterogeneous Networks

Disertační práce se zabývá kryptografickými protokoly poskytující ochranu soukromí, které jsou určeny pro zabezpečení komunikačních a informačních systémů tvořících heterogenní sítě. Práce se zaměřuje především na možnosti využití nekonvenčních kryptografických prostředků, které poskytují rozšířené bezpečnostní požadavky, jako je například ochrana soukromí uživatelů komunikačního systému. V práci je stanovena výpočetní náročnost kryptografických a matematických primitiv na různých zařízeních, které se podílí na zabezpečení heterogenní sítě. Hlavní cíle práce se zaměřují na návrh pokročilých kryptografických protokolů poskytujících ochranu soukromí. V práci jsou navrženy celkově tři protokoly, které využívají skupinových podpisů založených na bilineárním párování pro zajištění ochrany soukromí uživatelů. Tyto navržené protokoly zajišťují ochranu soukromí a nepopiratelnost po celou dobu datové komunikace spolu s autentizací a integritou přenášených zpráv. Pro navýšení výkonnosti navržených protokolů je využito optimalizačních technik, např. dávkového ověřování, tak aby protokoly byly praktické i pro heterogenní sítě.The dissertation thesis deals with privacy-preserving cryptographic protocols for secure communication and information systems forming heterogeneous networks. The thesis focuses on the possibilities of using non-conventional cryptographic primitives that provide enhanced security features, such as the protection of user privacy in communication systems. In the dissertation, the performance of cryptographic and mathematic primitives on various devices that participate in the security of heterogeneous networks is evaluated. The main objectives of the thesis focus on the design of advanced privacy-preserving cryptographic protocols. There are three designed protocols which use pairing-based group signatures to ensure user privacy. These proposals ensure the protection of user privacy together with the authentication, integrity and non-repudiation of transmitted messages during communication. The protocols employ the optimization techniques such as batch verification to increase their performance and become more practical in heterogeneous networks.

Location Privacy And Geo Based Applications

— Using Geo-social networking like Apple's i Groups and Hot Potato, many people communicate with their neighbouring locations through their associates and their suggestions. Without sufficient location protection, however, these systems can be easily misused, in this paper, we introduce, a technique that provides location secrecy without adding complexity into query results. Our idea here is to secure user-specific, coordinate conversion to all location data shared with the server. The associates of a user share this user’s secret key so they can apply the same conversion. This allows all spatial queries to be evaluated correctly by the server, but our privacy mechanisms guarantee that servers are unable to see or infer the actual location data from the transformed data or from the data access

When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy

In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and leakage of sensitive information, so it is necessary to protect the privacy of location information. For this purpose, people should know their location privacy preferences, that is, whether or not he/she can release location information at each place and time. However, it is not easy for each user to make such decisions and it is troublesome to set the privacy preference at each time. Therefore, we propose a method to recommend location privacy preferences for decision making. Comparing to existing method, our method can improve the accuracy of recommendation by using matrix factorization and preserve privacy strictly by local differential privacy, whereas the existing method does not achieve formal privacy guarantee. In addition, we found the best granularity of a location privacy preference, that is, how to express the information in location privacy protection. To evaluate and verify the utility of our method, we have integrated two existing datasets to create a rich information in term of user number. From the results of the evaluation using this dataset, we confirmed that our method can predict location privacy preferences accurately and that it provides a suitable method to define the location privacy preference

Approach for creating useful, gamified and social map applications utilising privacy-preserving crowdsourcing

The production and use of geographic information have become easier and more social. The interactivity of maps has fundamentally changed, not only because the touch-based interfaces are easier to use, but also because maps offer possibilities to interact with others. Map applications allow citizens to contribute but also share content to others. This contribution and sharing done by regular people is referred to as crowdsourcing. Map applications that utilise crowdsourcing face specific issues regarding the creation process, the usefulness and the crowdsourcing. These issues, however, have not been studied comprehensively and lack real world examples. This dissertation is the initial step to fill this gap by studying map applications that utilise crowdsourcing. These map applications are described using the design science research approach. Three issues relevant for the map application studied are: 1) the creation process, 2) utility requirements and usability heuristics, and 3) crowdsourcing approach. These issues are studied by using the design science research approach to produce theoretical and empirical knowledge of three map applications utilising crowdsourcing. The aim is to use this knowledge to form a design science research based approach suitable for creating map applications utilising crowdsourcing. The results regarding the creation process indicate that following a specific approach will help in creating crowdsourced map applications. This dissertation provides a customised design science research approach for creating crowdsourced map applications. Furthermore, prescriptive knowledge that provides real world examples crowdsourced map applications is provided. The results concerning the usefulness of map applications utilising crowdsourcing indicate that there are specific utility and usability requirements to be accounted for. This dissertation provides key utility requirements and usability heuristics for crowdsourced map applications. In general, a map interface for exploring and sharing content is needed. The map interface should be simple, citizens should be supported and interaction should be intuitive. The results concerning the crowdsourcing approach of map applications indicate that there is a need for specifying how citizens are involved in the process. This dissertation provides key requirements of the crowdsourcing approach of these types of map applications. The community driven crowdsourcing approach should be supported by official content and an engagement approach based on gamified and social elements to motivate content sharing. Privacy of citizens should be preserved by applying the privacy by design approach throughout the creation process. Privacy-preserving map applications utilising community-driven crowdsourcing, in which citizens can be engaged with gamification and social elements to explore and share content can be created by following the designs science research based approach presented in this dissertation.Geospatiaalisen eli paikkaan liittyvän tiedon tuotanto ja käyttö on helpottunut ja muuttunut yhä yhteisöllisemmäksi. Myös karttojen vuorovaikutteisuus on perustavanlaatuisesti muuttunut. Karttapohjaiset käyttöliittymät ovat yhä helppokäyttöisempiä ja niiden avulla kansalaiset voivat tuottaa tietoa, mutta myös jakaa sitä toisilleen. Tätä tavallisten kansalaisten tekemää tiedon tuottamista ja jakamista kutsutaan joukkoistamiseksi. Karttasovelluksiin, jotka hyödyntävät joukkoistettua tiedonkeruuta liittyy kuitenkin erityisiä haasteita niiden luomisen, hyödyllisyyden sekä joukkoistamisen osalta. Näitä haasteita ei ole vielä samanaikaisesti tutkittu kattavasti eikä näistä karttasovelluksista ole tarjolla tarpeeksi käytännön esimerkkejä ja tietoa. Tämä väitöskirja on ensimmäinen askel näiden haasteiden ratkaisemiseen, sillä tässä väitöskirjassa tutkitaan joukkoistamista hyödyntäviä karttasovelluksia. Väitöskirjassa perehdytään kolmeen karttasovelluksiin liittyvään haasteeseen, jotka ovat: 1) luomisprosessin lähestymistapa, 2) toiminnalliset vaatimukset ja käytettävyyden ohjeet ja 3) joukkoistamiseen käytetty lähestymistapa. Näitä haasteita tutkitaan tuottamalla tietoa kolmesta joukkoistamista hyödyntävästä karttasovelluksesta käyttäen kehitystutkimukseen perustuvaa tutkimusmenetelmää. Tätä tietoa käyttäen tavoitteena on muokata kehitystutkimukseen perustuvaa lähestymistapaa, jotta se soveltuisi joukkoistamista hyödyntävien karttasovellusten luomiseen. Luontiprosessin osalta tulokset osoittavat, että tieteellisen lähestymistavan seuraaminen helpottaa joukkoistettujen karttasovelluksien luomisessa. Väitöskirja ehdottaa muokattua kehitystytkimukseen perustuvaa lähestymistapaa joukkoistettujen karttasovellusten luomiseen. Lisäksi väitöskirja tarjoaa kuvailevia sekä ohjailevia tietoja joukkoistetuista karttasovelluksista käytännön esimerkein. Hyödyllisyyden osalta tulokset osoittavat, että joukkoistetuilla karttasovelluksilla on erityisiä toiminnallisia ja käytettävyyden vaatimuksia. Väitöskirja kokoaa keskeisiä toiminnallisia vaatimuksia sekä käytettävyyden ohjeita. Vaatimuksiin kuuluu helppokäyttöinen kansalaista tukeva karttakäyttöliittymä sisältöjen tutkimiseen sekä jakamiseen. Joukkoistamisen osalta tulokset osoittavat, että on tarve määritellä kuinka kansalaisen osallistuvat prosessiin. Tämä väitöskirja ehdottaa keskeisiä vaatimuksia lähestymistavalle joukkoistamiseen. Yhteisölähtöiseen joukkoistamiseen perustuvaa lähestymistapaa tulisi tukea karttasovelluksen sisällöillä, esimerkiksi kiinnostavalla taustakartalla. Lisäksi pelillisyyteen ja yhteisöllisyyteen perustuvalla sitouttamisella kansalaisia voidaan kannustaa sisältöjen jakamiseen. Kansalaisten yksityisyys tulisi turvata seuraamalla sisäänrakennetun tietosuojan lähestymistapaa läpi koko karttasovelluksen luomisprosessin ajan. Tässä väitöskirjassa esitettyä kehitystutkimukseen perustuvaa lähestymistapaa seuraamalla voidaan luoda yksityisyyden suojaavia ja yhteisölähtöistä joukkoistamista hyödyntäviä karttasovelluksia, joissa kansalaiset sitoutetaan pelillisyyden ja yhteisöllisyyden keinoin tutkimaan ja jakamaan sisältöjä

Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming

Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio

SORTING AREA SECURELY IN GRAPHICAL TRACK APPLICATION

Using geosocial applications, such as FourSquare, millions of people interact with their surroundings through their friends and their recommendations. Without adequate privacy protection, however, these systems can be easily misused, for example, to track users or target them for home invasion. In this paper, we introduce LocX, a novel alternative that provides significantly improvedlocation privacy without adding uncertainty into query results or relying on strong assumptions about server security. Our key insight is to apply secure user-specific, distance-preserving coordinate transformations to all location data shared with the server. The friends of a user share this user’s secrets so they can apply the same transformation. This allows all location queries to be evaluated correctly by the server, but our privacy mechanisms guarantee that servers are unable to see or infer the actual location data from the transformed data or from the data access. We show that LocX provides privacy even against a powerful adversary model, and we use prototype measurements to show that it provides privacy with very little performance overhead, making it suitable for today’s mobile devices

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe\u27s ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp