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's 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.

Multi-Party Trust Computation in Decentralized Environments

In this paper, we describe a decentralized privacy-preserving protocol for securely casting trust ratings in distributed reputation systems. Our protocol allows n participants to cast their votes in a way that preserves the privacy of individual values against both internal and external attacks. The protocol is coupled with an extensive theoretical analysis in which we formally prove that our protocol is resistant to collusion against as many as n-1 corrupted nodes in the semi-honest model. The behavior of our protocol is tested in a real P2P network by measuring its communication delay and processing overhead. The experimental results uncover the advantages of our protocol over previous works in the area; without sacrificing security, our decentralized protocol is shown to be almost one order of magnitude faster than the previous best protocol for providing anonymous feedback.

Secure and Scalable Statistical Computation of Questionnaire Data in R

Collecting data via a questionnaire and analyzing them while preserving respondents’ privacy may increase the number of respondents and the truthfulness of their responses. It may also reduce the systematic differences between respondents and non-respondents. In this paper, we propose a privacy-preserving method for collecting and analyzing survey responses using secure multi-party computation (SMC). The method is secure under the semi-honest adversarial model. The proposed method computes a wide variety of statistics. Total and stratified statistical counts are computed using the secure protocols developed in this paper. Then, additional statistics, such as a contingency table, a chi-square test, an odds ratio, and logistic regression, are computed within the R statistical environment using the statistical counts as building blocks. The method was evaluated on a questionnaire dataset of 3,158 respondents sampled for a medical study and simulated questionnaire datasets of up to 50,000 respondents. The computation time for the statistical analyses linearly scales as the number of respondents increases. The results show that the method is efficient and scalable for practical use. It can also be used for other applications in which categorical data are collected.

Personal Data and Encryption in the European General Data Protection Regulation

Encryption of personal data is widely regarded as a privacy preserving technology which could potentially play a key role for the compliance of innovative IT technology within the European data protection law framework. Therefore, in this paper, we examine the new EU General Data Protection Regulation’s relevant provisions regarding encryption – such as those for anonymisation and pseudonymisation – and assess whether encryption can serve as an anonymisation technique, which can lead to the non-applicability of the GDPR. However, the provisions of the GDPR regarding the material scope of the Regulation still leave space for legal uncertainty when determining whether a data subject is identifiable or not. Therefore, we inter alia assess the Opinion of the Advocate General of the European Court of Justice (ECJ) regarding a preliminary ruling on the interpretation of the dispute concerning whether a dynamic IP address can be considered as personal data, which may put an end to the dispute whether an absolute or a relative approach has to be used for the assessment of the identifiability of data subjects. Furthermore, we outline the issue of whether the anonymisation process itself constitutes a further processing of personal data which needs to have a legal basis in the GDPR. Finally, we give an overview of relevant encryption techniques and examine their impact upon the GDPR’s material scope.

Provable Security for Cryptocurrencies

The past several years have seen the surprising and rapid rise of Bitcoin and other “cryptocurrencies.” These are decentralized peer-to-peer networks that allow users to transmit money, tocompose financial instruments, and to enforce contracts between mutually distrusting peers, andthat show great promise as a foundation for financial infrastructure that is more robust, efficientand equitable than ours today. However, it is difficult to reason about the security of cryptocurrencies. Bitcoin is a complex system, comprising many intricate and subtly-interacting protocol layers. At each layer it features design innovations that (prior to our work) have not undergone any rigorous analysis. Compounding the challenge, Bitcoin is but one of hundreds of competing cryptocurrencies in an ecosystem that is constantly evolving. The goal of this thesis is to formally reason about the security of cryptocurrencies, reining in their complexity, and providing well-defined and justified statements of their guarantees. We provide a formal specification and construction for each layer of an abstract cryptocurrency protocol, and prove that our constructions satisfy their specifications. The contributions of this thesis are centered around two new abstractions: “scratch-off puzzles,” and the “blockchain functionality” model. Scratch-off puzzles are a generalization of the Bitcoin “mining” algorithm, its most iconic and novel design feature. We show how to provide secure upgrades to a cryptocurrency by instantiating the protocol with alternative puzzle schemes. We construct secure puzzles that address important and well-known challenges facing Bitcoin today, including wasted energy and dangerous coalitions. The blockchain functionality is a general-purpose model of a cryptocurrency rooted in the “Universal Composability” cryptography theory. We use this model to express a wide range of applications, including transparent “smart contracts” (like those featured in Bitcoin and Ethereum), and also privacy-preserving applications like sealed-bid auctions. We also construct a new protocol compiler, called Hawk, which translates user-provided specifications into privacy-preserving protocols based on zero-knowledge proofs.

Enhancing the Availability of Electronic Health Records for Secondary Purposes – A Case Study

Due to the sensitive nature of patient data, the secondary use of electronic health records (EHR) is restricted in scientific research and product development. Such restrictions pursue to preserve the privacy of respective patients by limiting the availability and variety of sensitive patient data. Current limitations do not correspond with the actual needs requested by the potential secondary users. In this thesis, the secondary use of Finnish and Swedish EHR data is explored for the purpose of enhancing the availability of such data for clinical research and product development. Involved EHR-related procedures and technologies are analysed to identify the issues limiting the secondary use of patient data. Successful secondary use of patient data increases the data value. To explore the identified circumstances, a case study of potential secondary users and use intentions regarding EHR data was carried out in Finland and Sweden. The data collection for the conducted case study was performed using semi-structured interviews. In total, 14 Finnish and Swedish experts representing scientific research, health management, and business were interviewed. The motivation for the corresponding interviews was to evaluate the protection of EHR data used for secondary purposes. The efficiency of implemented procedures and technologies was analysed in terms of data availability and privacy preserving. The results of the conducted case study show that the factors affecting EHR availability are divided to three categories: management of patient data, preservation of patients' privacy, and potential secondary users. Identified issues regarding data management included laborious and inconsistent data request procedures and the role and effect of external service providers. Based on the study findings, two secondary use approaches enabling the secondary use of EHR data are identified: data alteration and protected processing environment. Data alteration increases the availability of relevant EHR data, further decreasing the value of such data. Protected processing approach restricts the amount of potential users and use intentions while providing more valuable data content.

A secure and efficient data sharing framework with delegated capabilities in hybrid cloud

Hybrid cloud is a widely used cloud architecture in large companies that can outsource data to the publiccloud, while still supporting various clients like mobile devices. However, such public cloud data outsourcing raises serious security concerns, such as how to preserve data confidentiality and how to regulate access policies to the data stored in public cloud. To address this issue, we design a hybrid cloud architecture that supports data sharing securely and efficiently, even with resource-limited devices, where private cloud serves as a gateway between the public cloud and the data user. Under such architecture, we propose an improved construction of attribute-based encryption that has the capability of delegating encryption/decryption computation, which achieves flexible access control in the cloud and privacy-preserving in datautilization even with mobile devices. Extensive experiments show the scheme can further decrease the computational cost and space overhead at the user side, which is quite efficient for the user with limited mobile devices. In the process of delegating most of the encryption/decryption computation to private cloud, the user can not disclose any information to the private cloud. We also consider the communication securitythat once frequent attribute revocation happens, our scheme is able to resist some attacks between private cloud and data user by employing anonymous key agreement.

When compressive sensing meets data hiding

Personal use is permitted.We present a novel framework of performing multimedia data hiding using an over-complete dictionary, which brings compressive sensing to the application of data hiding. Unlike the conventional orthonormal full-space dictionary, the over-complete dictionary produces an underdetermined system with infinite transform results. We first discuss the minimum norm formulation (ℓ2-norm) which yields a closed-form solution and the concept of watermark projection, so that higher embedding capacity and an additional privacy preserving feature can be obtained. Furthermore, we study the sparse formulation (ℓ0-norm) and illustrate that as long as the ℓ0-norm of the sparse representation of the host signal is less than the signal's dimension in the original domain, an informed sparse domain data hiding system can be established by modifying the coefficients of the atoms that have not participated in representing the host signal. A single support modification-based data hiding system is then proposed and analyzed as an example. Several potential research directions are discussed for further studies. More generally, apart from the ℓ2 and ℓ0-norm constraints, other conditions for reliable detection performance are worth of future investigation.

Un modèle rétroactif de réconciliation utilité-confidentialité sur les données d’assurance

Le partage des données de façon confidentielle préoccupe un bon nombre d’acteurs, peu importe le domaine. La recherche évolue rapidement, mais le manque de solutions adaptées à la réalité d’une entreprise freine l’adoption de bonnes pratiques d’affaires quant à la protection des renseignements sensibles. Nous proposons dans ce mémoire une solution modulaire, évolutive et complète nommée PEPS, paramétrée pour une utilisation dans le domaine de l’assurance. Nous évaluons le cycle entier d’un partage confidentiel, de la gestion des données à la divulgation, en passant par la gestion des forces externes et l’anonymisation. PEPS se démarque du fait qu’il utilise la contextualisation du problème rencontré et l’information propre au domaine afin de s’ajuster et de maximiser l’utilisation de l’ensemble anonymisé. À cette fin, nous présentons un algorithme d’anonymat fortement contextualisé ainsi que des mesures de performances ajustées aux analyses d’expérience.

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's 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.

Un modèle rétroactif de réconciliation utilité-confidentialité sur les données d’assurance

Le partage des données de façon confidentielle préoccupe un bon nombre d’acteurs, peu importe le domaine. La recherche évolue rapidement, mais le manque de solutions adaptées à la réalité d’une entreprise freine l’adoption de bonnes pratiques d’affaires quant à la protection des renseignements sensibles. Nous proposons dans ce mémoire une solution modulaire, évolutive et complète nommée PEPS, paramétrée pour une utilisation dans le domaine de l’assurance. Nous évaluons le cycle entier d’un partage confidentiel, de la gestion des données à la divulgation, en passant par la gestion des forces externes et l’anonymisation. PEPS se démarque du fait qu’il utilise la contextualisation du problème rencontré et l’information propre au domaine afin de s’ajuster et de maximiser l’utilisation de l’ensemble anonymisé. À cette fin, nous présentons un algorithme d’anonymat fortement contextualisé ainsi que des mesures de performances ajustées aux analyses d’expérience.

Análisis de las barreras para la unificación de una Historia Clínica Electrónica -HCE- en Colombia

Objetivo: Identificar las barreras para la unificación de una Historia Clínica Electrónica –HCE- en Colombia. Materiales y Métodos: Se realizó un estudio cualitativo. Se realizaron entrevistas semiestructuradas a profesionales y expertos de 22 instituciones del sector salud, de Bogotá y de los departamentos de Cundinamarca, Santander, Antioquia, Caldas, Huila, Valle del Cauca. Resultados: Colombia se encuentra en una estructuración para la implementación de la Historia Clínica Electrónica Unificada -HCEU-. Actualmente, se encuentra en unificación en 42 IPSs públicas en el departamento de Cundinamarca, el desarrollo de la HCEU en el país es privado y de desarrollo propio debido a las necesidades particulares de cada IPS. Conclusiones: Se identificaron barreras humanas, financieras, legales, organizacionales, técnicas y profesionales en los departamentos entrevistados. Se identificó que la unificación de la HCE depende del acuerdo de voluntades entre las IPSs del sector público, privado, EPSs, y el Gobierno Nacional.

A generic framework for three-factor authentication : preserving security and privacy in distributed systems

As part of the security within distributed systems, various services and resources need protection from unauthorized use. Remote authentication is the most commonly used method to determine the identity of a remote client. This paper investigates a systematic approach for authenticating clients by three factors, namely password, smart card, and biometrics. A generic and secure framework is proposed to upgrade two-factor authentication to three-factor authentication. The conversion not only significantly improves the information assurance at low cost but also protects client privacy in distributed systems. In addition, our framework retains several practice-friendly properties of the underlying two-factor authentication, which we believe is of independent interest.