A Practical Framework for Storing and Searching Encrypted Data on Cloud Storage

Security has become a significant concern with the increased popularity of cloud storage services. It comes with the vulnerability of being accessed by third parties. Security is one of the major hurdles in the cloud server for the user when the user data that reside in local storage is outsourced to the cloud. It has given rise to security concerns involved in data confidentiality even after the deletion of data from cloud storage. Though, it raises a serious problem when the encrypted data needs to be shared with more people than the data owner initially designated. However, searching on encrypted data is a fundamental issue in cloud storage. The method of searching over encrypted data represents a significant challenge in the cloud. Searchable encryption allows a cloud server to conduct a search over encrypted data on behalf of the data users without learning the underlying plaintexts. While many academic SE schemes show provable security, they usually expose some query information, making them less practical, weak in usability, and challenging to deploy. Also, sharing encrypted data with other authorized users must provide each document's secret key. However, this way has many limitations due to the difficulty of key management and distribution. We have designed the system using the existing cryptographic approaches, ensuring the search on encrypted data over the cloud. The primary focus of our proposed model is to ensure user privacy and security through a less computationally intensive, user-friendly system with a trusted third party entity. To demonstrate our proposed model, we have implemented a web application called CryptoSearch as an overlay system on top of a well-known cloud storage domain. It exhibits secure search on encrypted data with no compromise to the user-friendliness and the scheme's functional performance in real-world applications.Comment: 146 Pages, Master's Thesis, 6 Chapters, 96 Figures, 11 Table

BMSQABSE: Design of a Bioinspired Model to Improve Security & QoS Performance for Blockchain-Powered Attribute-based Searchable Encryption Applications

Attribute-based searchable encryption (ABSE) is a sub-field of security models that allow intensive searching capabilities for cloud-based shared storage applications. ABSE Models require higher computational power, which limits their application to high-performance computing devices. Moreover, ABSE uses linear secret sharing scheme (LSSS), which requires larger storage when compared with traditional encryption models. To reduce computational complexity, and optimize storage cost, various researchers have proposed use of Machine Learning Models (MLMs), that assist in identification & removal of storage & computational redundancies. But most of these models use static reconfiguration, thus cannot be applied to large-scale deployments. To overcome this limitation, a novel combination of Grey Wolf Optimization (GWO) with Particle Swarm Optimization (PSO) model to improve Security & QoS performance for Blockchain-powered Attribute-based Searchable Encryption deployments is proposed in this text. The proposed model augments ABSE parameters to reduce its complexity and improve QoS performance under different real-time user request scenarios. It intelligently selects cyclic source groups with prime order & generator values to create bilinear maps that are used for ABSE operations. The PSO Model assists in generation of initial cyclic population, and verifies its security levels, QoS levels, and deployment costs under multiple real-time cloud scenarios. Based on this initial analysis, the GWO Model continuously tunes ABSE parameters in order to achieve better QoS & security performance levels via stochastic operations. The proposed BMSQABSE model was tested under different cloud configurations, and its performance was evaluated for healthcare deployments. Based on this evaluation, it was observed that the proposed model achieved 8.3% lower delay, with 4.9% lower energy consumption, 14.5% lower storage requirements when compared with standard ABSE models. It was able to mitigate Distributed Denial of Service (DDoS), Masquerading, Finney, and Sybil attacks, which assists in deploying the proposed model for QoS-aware highly secure deployments

AI-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT

Abstract: Please refer to full text to view abstrac

Secure Dynamic Cloud-based Collaboration with Hierarchical Access

In recent years, the Cloud has emerged as an attractive way of hosting and delivering services over the Internet. This has resulted in a renewed focus on information security in the case where data is stored in the virtual space of the cloud and is not physically accessible to the customer. Through this thesis the boundaries of securing data in a cloud context, while retaining the benefits of the cloud, are explored. The thesis addresses the increasing security concerns of migrating to the cloud andutilising it for data storage.The research of this thesis is divided into three separate areas: securing data in an untrusted cloud environment, ensuring data access control in the cloud, and securing data outside the cloud in the user's environment. Each area is addressed by separate conceptual designs. Together these comprise a secure dynamic cloud-based collaboration environment with hierarchical access. To further validate the conceptual designs, proof of concept prototypes have been constructed.The conceptual designs have been devised by exploring and extending the boundaries of existing secure data-storage schemes, and then combining these with well-known security principles and cutting-edge research within the field of cryptography. The results of this thesis are feasible conceptual designs for a cloud-based dynamic collaboration environment. The conceptual designs address the challenges of secure cloud-based storage and allow the benefits of cloud-based storage to be utilised. Furthermore, this thesis provides a solid foundation for further work within this field

Privacy-Preserving Content-Based Image Retrieval in the Cloud (Extended Version)

Storage requirements for visual data have been increasing in recent years, following the emergence of many new highly interactive multimedia services and applications for both personal and corporate use. This has been a key driving factor for the adoption of cloud-based data outsourcing solutions. However, outsourcing data storage to the Cloud also leads to new challenges that must be carefully addressed, especially regarding privacy. In this paper we propose a secure framework for outsourced privacy-preserving storage and retrieval in large image repositories. Our proposal is based on IES-CBIR, a novel Image Encryption Scheme that displays Content-Based Image Retrieval properties. Our solution enables both encrypted storage and searching using CBIR queries while preserving privacy. We have built a prototype of the proposed framework, formally analyzed and proven its security properties, and experimentally evaluated its performance and precision. Our results show that IES-CBIR is provably secure, allows more efficient operations than existing proposals, both in terms of time and space complexity, and enables more reliable practical application scenarios

Secure and Efficient Comparisons between Untrusted Parties

A vast number of online services is based on users contributing their personal information. Examples are manifold, including social networks, electronic commerce, sharing websites, lodging platforms, and genealogy. In all cases user privacy depends on a collective trust upon all involved intermediaries, like service providers, operators, administrators or even help desk staff. A single adversarial party in the whole chain of trust voids user privacy. Even more, the number of intermediaries is ever growing. Thus, user privacy must be preserved at every time and stage, independent of the intrinsic goals any involved party. Furthermore, next to these new services, traditional offline analytic systems are replaced by online services run in large data centers. Centralized processing of electronic medical records, genomic data or other health-related information is anticipated due to advances in medical research, better analytic results based on large amounts of medical information and lowered costs. In these scenarios privacy is of utmost concern due to the large amount of personal information contained within the centralized data. We focus on the challenge of privacy-preserving processing on genomic data, specifically comparing genomic sequences. The problem that arises is how to efficiently compare private sequences of two parties while preserving confidentiality of the compared data. It follows that the privacy of the data owner must be preserved, which means that as little information as possible must be leaked to any party participating in the comparison. Leakage can happen at several points during a comparison. The secured inputs for the comparing party might leak some information about the original input, or the output might leak information about the inputs. In the latter case, results of several comparisons can be combined to infer information about the confidential input of the party under observation. Genomic sequences serve as a use-case, but the proposed solutions are more general and can be applied to the generic field of privacy-preserving comparison of sequences. The solution should be efficient such that performing a comparison yields runtimes linear in the length of the input sequences and thus producing acceptable costs for a typical use-case. To tackle the problem of efficient, privacy-preserving sequence comparisons, we propose a framework consisting of three main parts. a) The basic protocol presents an efficient sequence comparison algorithm, which transforms a sequence into a set representation, allowing to approximate distance measures over input sequences using distance measures over sets. The sets are then represented by an efficient data structure - the Bloom filter -, which allows evaluation of certain set operations without storing the actual elements of the possibly large set. This representation yields low distortion for comparing similar sequences. Operations upon the set representation are carried out using efficient, partially homomorphic cryptographic systems for data confidentiality of the inputs. The output can be adjusted to either return the actual approximated distance or the result of an in-range check of the approximated distance. b) Building upon this efficient basic protocol we introduce the first mechanism to reduce the success of inference attacks by detecting and rejecting similar queries in a privacy-preserving way. This is achieved by generating generalized commitments for inputs. This generalization is done by treating inputs as messages received from a noise channel, upon which error-correction from coding theory is applied. This way similar inputs are defined as inputs having a hamming distance of their generalized inputs below a certain predefined threshold. We present a protocol to perform a zero-knowledge proof to assess if the generalized input is indeed a generalization of the actual input. Furthermore, we generalize a very efficient inference attack on privacy-preserving sequence comparison protocols and use it to evaluate our inference-control mechanism. c) The third part of the framework lightens the computational load of the client taking part in the comparison protocol by presenting a compression mechanism for partially homomorphic cryptographic schemes. It reduces the transmission and storage overhead induced by the semantically secure homomorphic encryption schemes, as well as encryption latency. The compression is achieved by constructing an asymmetric stream cipher such that the generated ciphertext can be converted into a ciphertext of an associated homomorphic encryption scheme without revealing any information about the plaintext. This is the first compression scheme available for partially homomorphic encryption schemes. Compression of ciphertexts of fully homomorphic encryption schemes are several orders of magnitude slower at the conversion from the transmission ciphertext to the homomorphically encrypted ciphertext. Indeed our compression scheme achieves optimal conversion performance. It further allows to generate keystreams offline and thus supports offloading to trusted devices. This way transmission-, storage- and power-efficiency is improved. We give security proofs for all relevant parts of the proposed protocols and algorithms to evaluate their security. A performance evaluation of the core components demonstrates the practicability of our proposed solutions including a theoretical analysis and practical experiments to show the accuracy as well as efficiency of approximations and probabilistic algorithms. Several variations and configurations to detect similar inputs are studied during an in-depth discussion of the inference-control mechanism. A human mitochondrial genome database is used for the practical evaluation to compare genomic sequences and detect similar inputs as described by the use-case. In summary we show that it is indeed possible to construct an efficient and privacy-preserving (genomic) sequences comparison, while being able to control the amount of information that leaves the comparison. To the best of our knowledge we also contribute to the field by proposing the first efficient privacy-preserving inference detection and control mechanism, as well as the first ciphertext compression system for partially homomorphic cryptographic systems

Securing clouds using cryptography and traffic classification

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Over the last decade, cloud computing has gained popularity and wide acceptance, especially within the health sector where it offers several advantages such as low costs, flexible processes, and access from anywhere. Although cloud computing is widely used in the health sector, numerous issues remain unresolved. Several studies have attempted to review the state of the art in eHealth cloud privacy and security however, some of these studies are outdated or do not cover certain vital features of cloud security and privacy such as access control, revocation and data recovery plans. This study targets some of these problems and proposes protocols, algorithms and approaches to enhance the security and privacy of cloud computing with particular reference to eHealth clouds. Chapter 2 presents an overview and evaluation of the state of the art in eHealth security and privacy. Chapter 3 introduces different research methods and describes the research design methodology and processes used to carry out the research objectives. Of particular importance are authenticated key exchange and block cipher modes. In Chapter 4, a three-party password-based authenticated key exchange (TPAKE) protocol is presented and its security analysed. The proposed TPAKE protocol shares no plaintext data; all data shared between the parties are either hashed or encrypted. Using the random oracle model (ROM), the security of the proposed TPAKE protocol is formally proven based on the computational Diffie-Hellman (CDH) assumption. Furthermore, the analysis included in this chapter shows that the proposed protocol can ensure perfect forward secrecy and resist many kinds of common attacks such as man-in-the-middle attacks, online and offline dictionary attacks, replay attacks and known key attacks. Chapter 5 proposes a parallel block cipher (PBC) mode in which blocks of cipher are processed in parallel. The results of speed performance tests for this PBC mode in various settings are presented and compared with the standard CBC mode. Compared to the CBC mode, the PBC mode is shown to give execution time savings of 60%. Furthermore, in addition to encryption based on AES 128, the hash value of the data file can be utilised to provide an integrity check. As a result, the PBC mode has a better speed performance while retaining the confidentiality and security provided by the CBC mode. Chapter 6 applies TPAKE and PBC to eHealth clouds. Related work on security, privacy preservation and disaster recovery are reviewed. Next, two approaches focusing on security preservation and privacy preservation, and a disaster recovery plan are proposed. The security preservation approach is a robust means of ensuring the security and integrity of electronic health records and is based on the PBC mode, while the privacy preservation approach is an efficient authentication method which protects the privacy of personal health records and is based on the TPAKE protocol. A discussion about how these integrated approaches and the disaster recovery plan can ensure the reliability and security of cloud projects follows. Distributed denial of service (DDoS) attacks are the second most common cybercrime attacks after information theft. The timely detection and prevention of such attacks in cloud projects are therefore vital, especially for eHealth clouds. Chapter 7 presents a new classification system for detecting and preventing DDoS TCP flood attacks (CS_DDoS) for public clouds, particularly in an eHealth cloud environment. The proposed CS_DDoS system offers a solution for securing stored records by classifying incoming packets and making a decision based on these classification results. During the detection phase, CS_DDOS identifies and determines whether a packet is normal or from an attacker. During the prevention phase, packets classified as malicious are denied access to the cloud service, and the source IP is blacklisted. The performance of the CS_DDoS system is compared using four different classifiers: a least-squares support vector machine (LS-SVM), naïve Bayes, K-nearest-neighbour, and multilayer perceptron. The results show that CS_DDoS yields the best performance when the LS-SVM classifier is used. This combination can detect DDoS TCP flood attacks with an accuracy of approximately 97% and a Kappa coefficient of 0.89 when under attack from a single source, and 94% accuracy and a Kappa coefficient of 0.9 when under attack from multiple attackers. These results are then discussed in terms of the accuracy and time complexity, and are validated using a k-fold cross-validation model. Finally, a method to mitigate DoS attacks in the cloud and reduce excessive energy consumption through managing and limiting certain flows of packets is proposed. Instead of a system shutdown, the proposed method ensures the availability of service. The proposed method manages the incoming packets more effectively by dropping packets from the most frequent requesting sources. This method can process 98.4% of the accepted packets during an attack. Practicality and effectiveness are essential requirements of methods for preserving the privacy and security of data in clouds. The proposed methods successfully secure cloud projects and ensure the availability of services in an efficient way

Security protocols for mobile ubiquitous e-health systems

Mención Internacional en el título de doctorWearable and implantable medical devices constitute an already established industry nowadays. According to a recent research [113], North America is currently the most important market followed by Europe, Asia-Pacific and the rest of the world. Additionally, the same document remarks the importance of the Asia-Pacific region due to the rising ageing population and the overpopulation in that area. The most common implantable medical devices include pacemakers, defibrillators, cochlear implants, insulin pumps, and neurostimulators among others. In recent years, the proliferation of smartphones and other mobile “smart” devices with substantial computational and communication capabilities have reshaped the way wireless body area network may be implemented. In their current generation (or in a near future), all of them share a common feature: wireless communication capabilities [127]. Moreover, implantable medical devices have the ability to support and store telemetry data facilitating the remote monitoring of the patient. Medical devices can be part of a wireless body area network, operating both as sensors and as actuators and making decisions in real time. On the other hand, a new kind of devices called wearables such as smart bracelets or smart watches have been equipped with several sensors like Photoplethysmogram (PPG) to record the heart beats, accelerometers to count the steps or Global Positioning System (GPS) to geopositioning users and were originally conceived as cheap solutions to help people to improve their workout. However these devices have demonstrated to be quite useful in many healthcare environments due to a huge variety of different and low-cost medical sensors. Thus, patients can be monitored for long periods of time without interfering in their daily life and taking their vital signs constantly under control. Security and privacy issues have been described as two of the most challenging problems of implantable medical devices and, more generally, wireless body area networks [6, 47, 84, 103]. As an example, it has been demonstrated that somebody equipped with a low cost device can eavesdrop on the data exchanged between a reader and a peacemaker and may even induce a cardiac arrest [71]. Health-related data have been the focus of several attacks almost since the adoption of computers in the healthcare domain. As a recent example, in 2010 personal data from more than 26 million of veterans were stolen from the Department of Veterans Affairs’ database in the US by an employee who had access to the database [104]. The Ponemon Institute pointed out that Germany and the US spent in 2013 more than $7.56 and$11 millions, respectively, to protect personal health records from attacks. This PhD dissertation explores the security and privacy of data in healthcare environments where confidential information is measured in real time by some sensors placed in, on, or around the human body. Security and privacy in medical conditions have been widely studied by the research community, nonetheless with the recent boom of wearable devices, new security issues have arisen. The first part of this dissertation is dedicated to the introduction and to expose both the main motivation and objectives of this PhD Thesis. Additionally the contributions and the organization of this document are also presented. In the second part a recent proposal has been analysed from the security and privacy points of view. From this study, vulnerabilities concerning to full disclosure, impersonation, traceability, de-synchronization, and Denial-of-Service (DoS) attacks have been found. These attacks make the protocol infeasible to be introduced with an adequate security and sufficient privacy protection level. Finally, a new protocol named Fingerprint⁺ protocol for Internet of Thing (IoT) is presented, which is based on ISO/IEC 9798-2 and ISO/IEC 18000-6C and whose security is formally verified using BAN logic. In the third part of this dissertation, a new system based on International Standard Organization (ISO) standards and security National Institute of Standards and Technology (NIST) recommendations have been proposed. First, we present a mutual entity authentication protocol inspired on ISO/IEC 9798 Part 2. This system could be deployed in a hospital where Radio Frequency IDentification (RFID) technology may be used to prune blood-handling errors, i.e., the identities of the patients and blood bags are confirmed (authentication protocol) and after that the matching between both entities is checked (verification step). Second, a secure messaging protocol inspired on ISO/IEC 11770 Part 2 and similar to that used in electronic passports is presented. Nowadays the new generation of medical implants possess wireless connectivity. Imagine a doctor equipped with a reader aims to access the records of vital signals stored on the memory of an implant. In this scenario, the doctor (reader) and the patient (implant) are first mutually authenticated and then a secure exchange of data can be performed. The fourth part of this Thesis provides an architecture based on two cryptographic protocols, the first one is for publishing personal data in a body area network composed of different sensors whereas the second one is designed for sending commands to those sensors by guaranteeing the confidentiality and fine-grained access control to the private data. Both protocols are based on a recently proposed public cryptography paradigm named ciphertext policy attribute-based encryption scheme which is lightweight enough to be embedded into wearable devices and sensors. Contrarily to other proposals made on this field, this architecture allows sensors not only to encrypt data but also to decrypt messages generated by other devices. The fifth part presents a new decentralized attribute based encryption scheme named Decentralized Ciphertext-Policy Attribute Based Searchable Encryption that incorporates ciphertext-policy attribute-based encryption with keyword search over encrypted data. This scheme allows users to (a) encrypt their personal data collected by a Wireless Body Area Network (WBAN) according to a policy of attributes; (b) define a set of keywords to enable other users (e.g., hospital stuff) to perform encrypted search over their personal (encrypted) data; (c) securely store the encrypted data on a semi-honest server and let the semi-honest server run the (encrypted) keyword search. Note that any user can perform a keyword query on the encrypted data, however the decryption of the resulting ciphertexts is possible only for users whose attribute satisfy the policy with which the data had been encrypted. We state and prove the security of our scheme against an honest-but-curious server and a passive adversary. Finally, we implement our system on heterogeneous devices and demonstrate its efficiency and scalability. Finally, this document ends with a conclusions achieved during this PhD and a summary of the main published contributions.Los dispositivos médicos implantables como los marcapasos o las bombas de insulina fueron concebidas originalmente para controlar automáticamente ciertos parámetros biológicos y, llegado el caso, poder actuar ante comportamientos anómalos como ataques cardíacos o episodios de hipoglucemia. Recientemente, han surgido uno dispositivos llamados wearables como las pulseras cuantificadoras, los relojes inteligentes o las bandas pectorales. Estos dispositivos han sido equipados con un número de sensores con capacidad de monitorizar señales vitales como el ritmo cardíaco, los movimientos (acelerómetros) o sistemas de posicionamiento (GPS) entre otros muchas opciones, siendo además una solución asequible y accesible para todo el mundo. A pesar de que el propósito original fue la mejora del rendimiento en actividades deportivas, estos dispositivos han resultado ser de gran utilidad en entornos médicos debido a su amplia variedad de sensores. Esta tecnología puede ayudar al personal médico a realizar seguimientos personalizados, constantes y en tiempo real del comportamiento de los pacientes, sin necesidad de interferir en sus vidas cotidianas. Esta Tesis doctoral está centrada en la seguridad y privacidad en entornos médicos, donde la información es recogida en tiempo real a través de una serie de sensores que pueden estar implantados o equipados en el propio paciente. La seguridad y la privacidad en entornos médicos ha sido el foco de muchos investigadores, no obstante con el reciente auge de los wearables se han generado nuevos retos debido a que son dispositivos con fuertes restricciones de cómputo, de memoria, de tamaño o de autonomía. En la primera parte de este documento, se introduce el problema de la seguridad y la privacidad en el paradigma de Internet de las cosas y haciendo especial hincapié en los entornos médicos. La motivación así como los principales objetivos y contribuciones también forman parte de este primer capítulo introductorio. La segunda parte de esta Tesis presenta un nuevo protocolo de autenticación basado en RFID para IoT. Este capítulo analiza previamente, desde el punto de vista de la seguridad y la privacidad un protocolo publicado recientemente y, tras demostrar que carece de las medidas de seguridad suficientes, un nuevo protocolo llamado Fingerprint⁺ compatible con los estándares de seguridad definidos en el estándar ISO/IEC 9798-2 y EPC-C1G2 (equivalente al estándard ISO/IEC 18000-6C) ha sido propuesto. Un nuevo sistema basado en estándares ISO y en recomendaciones realizadas por el NIST ha sido propuesto en la tercera parte de esta Tesis. En este capítulo se presentan dos protocolos bien diferenciados, el primero de ellos consiste en un protocolo de autenticación basado en el estándar ISO/IEC 9798 Part 2. A modo de ejemplo, este protocolo puede evitar problemas de compatibilidad sanguínea, es decir, primero se confirma que el paciente es quien dice ser y que la bolsa de sangre realmente contiene sangre (proceso de autenticación). Posteriormente se comprueba que esa bolsa de sangre va a ser compatible con el paciente (proceso de verificación). El segundo de los protocolos propuestos consiste en un protocolo seguro para el intercambio de información basado en el estándar ISO/IEC 11770 Part 2 (el mismo que los pasaportes electrónicos). Siguiendo con el ejemplo médico, imaginemos que un doctor equipado con un lector de radiofrecuencia desea acceder a los datos que un dispositivo implantado en el paciente está recopilando. En este escenario tanto el lector como el implante, se deben autenticar mutuamente para poder realizar el intercambio de información de manera segura. En el cuarto capítulo, una nueva arquitectura basada en el modelo de Publish/Subscribe ha sido propuesto. Esta solución está compuesta de dos protocolos, uno para el intercambio de información en una red de área personal y otro para poder reconfigurar el comportamiento de los sensores. Ambos protocolos están diseñados para garantizar tanto la seguridad como la privacidad de todos los datos que se envían en la red. Para ello, el sistema está basado en un sistema de criptografía de clave pública llamado Attribute Based Encryption que es suficientemente ligero y versátil como para ser implementado en dispositivos con altas restricciones de cómputo y de memoria. A continuación, en el quinto capítulo se propone una solución completamente orientada a entornos médicos donde la información que los sensores obtienen de los pacientes es cifrada y almacenada en servidores públicos. Una vez en estos servidores, cualquier usuario con privilegios suficientes puede realizar búsquedas sobre datos cifrados, obtener la información y descifrarla. De manera adicional, antes de que los datos cifrados se manden a la nube, el paciente puede definir una serie de palabras claves que se enlazarán a los datos para permitir posteriormente búsquedas y así obtener la información relacionada a un tema en concreto de manera fácil y eficiente. El último capítulo de esta Tesis se muestran las principales conclusiones obtenidas así como un resumen de las contribuciones científicas publicadas durante el período doctoral.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Arturo Ribagorda Garnacho.- Secretario: Jorge Blasco Alís.- Vocal: Jesús Garicia López de Lacall