A HYBRIDIZED ENCRYPTION SCHEME BASED ON ELLIPTIC CURVE CRYPTOGRAPHY FOR SECURING DATA IN SMART HEALTHCARE

Recent developments in smart healthcare have brought us a great deal of convenience. Connecting common objects to the Internet is made possible by the Internet of Things (IoT). These connected gadgets have sensors and actuators for data collection and transfer. However, if users' private health information is compromised or exposed, it will seriously harm their privacy and may endanger their lives. In order to encrypt data and establish perfectly alright access control for such sensitive information, attribute-based encryption (ABE) has typically been used. Traditional ABE, however, has a high processing overhead. As a result, an effective security system algorithm based on ABE and Fully Homomorphic Encryption (FHE) is developed to protect health-related data. ABE is a workable option for one-to-many communication and perfectly alright access management of encrypting data in a cloud environment. Without needing to decode the encrypted data, cloud servers can use the FHE algorithm to take valid actions on it. Because of its potential to provide excellent security with a tiny key size, elliptic curve cryptography (ECC) algorithm is also used. As a result, when compared to related existing methods in the literature, the suggested hybridized algorithm (ABE-FHE-ECC) has reduced computation and storage overheads. A comprehensive safety evidence clearly shows that the suggested method is protected by the Decisional Bilinear Diffie-Hellman postulate. The experimental results demonstrate that this system is more effective for devices with limited resources than the conventional ABE when the system’s performance is assessed by utilizing standard model

Data security in cloud storage services

Cloud Computing is considered to be the next-generation architecture for ICT where it moves the application software and databases to the centralized large data centers. It aims to offer elastic IT services where clients can benefit from significant cost savings of the pay-per-use model and can easily scale up or down, and do not have to make large investments in new hardware. However, the management of the data and services in this cloud model is under the control of the provider. Consequently, the cloud clients have less control over their outsourced data and they have to trust cloud service provider to protect their data and infrastructure from both external and internal attacks. This is especially true with cloud storage services. Nowadays, users rely on cloud storage as it offers cheap and unlimited data storage that is available for use by multiple devices (e.g. smart phones, tablets, notebooks, etc.). Besides famous cloud storage providers, such as Amazon, Google, and Microsoft, more and more third-party cloud storage service providers are emerging. These services are dedicated to offering more accessible and user friendly storage services to cloud customers. Examples of these services include Dropbox, Box.net, Sparkleshare, UbuntuOne or JungleDisk. These cloud storage services deliver a very simple interface on top of the cloud storage provided by storage service providers. File and folder synchronization between different machines, sharing files and folders with other users, file versioning as well as automated backups are the key functionalities of these emerging cloud storage services. Cloud storage services have changed the way users manage and interact with data outsourced to public providers. With these services, multiple subscribers can collaboratively work and share data without concerns about their data consistency, availability and reliability. Although these cloud storage services offer attractive features, many customers have not adopted these services. Since data stored in these services is under the control of service providers resulting in confidentiality and security concerns and risks. Therefore, using cloud storage services for storing valuable data depends mainly on whether the service provider can offer sufficient security and assurance to meet client requirements. From the way most cloud storage services are constructed, we can notice that these storage services do not provide users with sufficient levels of security leading to an inherent risk on users\u27 data from external and internal attacks. These attacks take the form of: data exposure (lack of data confidentiality); data tampering (lack of data integrity); and denial of data (lack of data availability) by third parties on the cloud or by the cloud provider himself. Therefore, the cloud storage services should ensure the data confidentiality in the following state: data in motion (while transmitting over networks), data at rest (when stored at provider\u27s disks). To address the above concerns, confidentiality and access controllability of outsourced data with strong cryptographic guarantee should be maintained. To ensure data confidentiality in public cloud storage services, data should be encrypted data before it is outsourced to these services. Although, users can rely on client side cloud storage services or software encryption tools for encrypting user\u27s data; however, many of these services fail to achieve data confidentiality. Box, for example, does not encrypt user files via SSL and within Box servers. Client side cloud storage services can intentionally/unintentionally disclose user decryption keys to its provider. In addition, some cloud storage services support convergent encryption for encrypting users\u27 data exposing it to “confirmation of a file attack. On the other hand, software encryption tools use full-disk encryption (FDE) which is not feasible for cloud-based file sharing services, because it encrypts the data as virtual hard disks. Although encryption can ensure data confidentiality; however, it fails to achieve fine-grained access control over outsourced data. Since, public cloud storage services are managed by un-trusted cloud service provider, secure and efficient fine-grained access control cannot be realized through these services as these policies are managed by storage services that have full control over the sharing process. Therefore, there is not any guarantee that they will provide good means for efficient and secure sharing and they can also deduce confidential information about the outsourced data and users\u27 personal information. In this work, we would like to improve the currently employed security measures for securing data in cloud store services. To achieve better data confidentiality for data stored in the cloud without relying on cloud service providers (CSPs) or putting any burden on users, in this thesis, we designed a secure cloud storage system framework that simultaneously achieves data confidentiality, fine-grained access control on encrypted data and scalable user revocation. This framework is built on a third part trusted (TTP) service that can be employed either locally on users\u27 machine or premises, or remotely on top of cloud storage services. This service shall encrypts users data before uploading it to the cloud and decrypts it after downloading from the cloud; therefore, it remove the burden of storing, managing and maintaining encryption/decryption keys from data owner\u27s. In addition, this service only retains user\u27s secret key(s) not data. Moreover, to ensure high security for these keys, it stores them on hardware device. Furthermore, this service combines multi-authority ciphertext policy attribute-based encryption (CP-ABE) and attribute-based Signature (ABS) for achieving many-read-many-write fine-grained data access control on storage services. Moreover, it efficiently revokes users\u27 privileges without relying on the data owner for re-encrypting massive amounts of data and re-distributing the new keys to the authorized users. It removes the heavy computation of re-encryption from users and delegates this task to the cloud service provider (CSP) proxy servers. These proxy servers achieve flexible and efficient re-encryption without revealing underlying data to the cloud. In our designed architecture, we addressed the problem of ensuring data confidentiality against cloud and against accesses beyond authorized rights. To resolve these issues, we designed a trusted third party (TTP) service that is in charge of storing data in an encrypted format in the cloud. To improve the efficiency of the designed architecture, the service allows the users to choose the level of severity of the data and according to this level different encryption algorithms are employed. To achieve many-read-many-write fine grained access control, we merge two algorithms (multi-authority ciphertext policy attribute-based encryption (MA- CP-ABE) and attribute-based Signature (ABS)). Moreover, we support two levels of revocation: user and attribute revocation so that we can comply with the collaborative environment. Last but not least, we validate the effectiveness of our design by carrying out a detailed security analysis. This analysis shall prove the correctness of our design in terms of data confidentiality each stage of user interaction with the cloud

Conditional Attribute-Based Proxy Re-Encryption

Proxy re-encryption (PRE) is a cryptographic primitive that allows a semi-trusted proxy to transfer the decryption rights of ciphertexts in a secure and privacy-preserving manner. This versatile primitive has been extended to several powerful variants, leading to numerous applications, such as e-mail forwarding and content distribution. One such variant is attribute-based PRE (AB-PRE), which provides an expressible access control mechanism by allowing the proxy to switch the underlying policy of an attribute-based encryption (ABE) ciphertext. However, the function of AB-PRE is to convert the underlying policies of all ciphertexts indiscriminately, which lacks the flexibility of ciphertext transformation. Therefore, AB-PRE needs to support the property of conditional delegation. Among the other variants of PRE, there is a variant called conditional PRE (C-PRE), which allows fine-grained delegations by restricting the proxy to performing valid re-encryption only for a limited set of ciphertexts. Unfortunately, existing PRE schemes cannot simultaneously achieve expressible access control mechanisms and fine-grained delegations. Specifically, we require a PRE scheme, via which the proxy can convert the underlying policies of an ABE ciphertext only if this ciphertext is in the set of ciphertexts allowing the proxy to perform valid transformations. To address this problem, we formalize the notion of conditional attribute-based PRE (CAB-PRE) in the honest re-encryption attacks (HRA) model, which is more robust and implies chosen-plaintext attacks (CPA) security, and propose the first CAB-PRE scheme. To construct such a scheme, we design as a building block, the first adaptively HRA-secure (ciphertext-policy) AB-PRE based on the learning with errors (LWE) problem. This scheme solves the open problem left by Susilo et al. in ESORICS\u2721 about how to construct an HRA-secure (ciphertext-policy) AB-PRE scheme, and it should be of independent interest. Then, we introduce a well-matched conditional delegation mechanism for this AB-PRE scheme to derive our adaptively HRA-secure CAB-PRE scheme

Secure and practical computation on encrypted data

Because of the importance of computing on data with privacy protections, the cryptographic community has developed both theoretical and practical solutions to compute on encrypted data. On the one hand, theoretical schemes, such as fully homomorphic encryption and functional encryption, are secure but extremely inefficient. On the other hand, practical schemes, such as property-preserving encryption, gain efficiency by accepting significant reductions in security. In this thesis, we first study the security of popular property-preserving encryption schemes that are being used by companies such as Microsoft and Google. We show that such schemes are unacceptably insecure for key target applications such as electronic medical records. Second, we propose new models to compute on encrypted data and develop efficient constructions and systems. We propose a new cryptographic primitive called Blind Storage and show how it can be used to realize symmetric searchable encryption, which is much more secure than property-preserving encryption. Finally, we propose a new cryptographic model called Controlled Functional Encryption and develop two efficient schemes in this model

Cryptographic techniques for privacy and access control in cloud-based applications

Digitization is one of the key challenges for today’s industries and society. It affects more and more business areas and also user data and, in particular, sensitive information. Due to its sensitivity, it is important to treat personal information as secure and private as possible yet enabling cloud-based software to use that information when requested by the user. In this thesis, we focus on the privacy-preserving outsourcing and sharing of data, the querying of outsourced protected data, and the usage of personal information as an access control mechanism for rating platforms, which should be protected from coercion attacks. In those three categories, we present cryptographic techniques and protocols that push the state of the art. In particular, we first present multi-client oblivious RAM (ORAM), which augments standard ORAM with selective data sharing through access control, confidentiality, and integrity. Second, we investigate on recent work in frequency-hiding order-preserving encryption and show that the state of the art misses rigorous treatment, allowing for simple attacks against the security of the existing scheme. As a remedy, we show how to fix the security definition and that the existing scheme, slightly adapted, fulfills it. Finally, we design and develop a coercion-resistant rating platform. Coercion-resistance has been dealt with mainly in the context of electronic voting yet also affects other areas of digital life such as rating platforms.Die Digitalisierung ist eine der größten Herausforderungen für Industrie und Gesellschaft. Neben vielen Geschäftsbereichen betrifft diese auch, insbesondere sensible, Nutzerdaten. Daher sollten persönliche Informationen so gut wie möglich gesichert werden. Zugleich brauchen Cloud-basierte Software-Anwendungen, die der Nutzer verwenden möchte, Zugang zu diesen Daten. Diese Dissertation fokussiert sich auf das sichere Auslagern und Teilen von Daten unter Wahrung der Privatsphäre, auf das Abfragen von geschützten, ausgelagerten Daten und auf die Nutzung persönlicher Informationen als Zugangsberechtigung für erpressungsresistente Bewertungsplattformen. Zu diesen drei Themen präsentieren wir kryptographische Techniken und Protokolle, die den Stand der Technik voran treiben. Der erste Teil stellt Multi-Client Oblivious RAM (ORAM) vor, das ORAM durch die Möglichkeit, Daten unter Wahrung von Vertraulichkeit und Integrität mit anderen Nutzern zu teilen, erweitert. Der zweite Teil befasst sich mit Freuquency-hiding Order-preserving Encryption. Wir zeigen, dass dem Stand der Technik eine formale Betrachtung fehlt, was zu Angriffen führt. Um Abhilfe zu schaffen, verbessern wir die Sicherheitsdefinition und beweisen, dass das existierende Verschlüsselungsschema diese durch minimale Änderung erfüllt. Abschließend entwickeln wir ein erpressungsresistentes Bewertungsportal. Erpressungsresistenz wurde bisher hauptsächlich im Kontext von elektronischen Wahlen betrachtet

RESCUE: Evaluation of a Fragmented Secret Share System in Distributed-Cloud Architecture

Scaling big data infrastructure using multi-cloud environment has led to the demand for highly secure, resilient and reliable data sharing method. Several variants of secret sharing scheme have been proposed but there remains a gap in knowledge on the evaluation of these methods in relation to scalability, resilience and key management as volume of files generated increase and cloud outages persist. In line with these, this thesis presents an evaluation of a method that combines data fragmentation with Shamir’s secret sharing scheme known as Fragmented Secret Share System (FSSS). It applies data fragmentation using a calculated optimum fragment size and encrypts each fragment using a 256-bit AES key length before dispersal to cloudlets, the encryption key is managed using secret sharing methods as used in cryptography.Four experiments were performed to measure the scalability, resilience and reliability in key management. The first and second experiments evaluated scalability using defined fragment blocks and an optimum fragment size. These fragment types were used to break file of varied sizes into fragments, and then encrypted and dispersed to the cloud, and recovered when required. Both were used in combination of different secret sharing policies for key management. The third experiment tested file recovery during cloud failures, while the fourth experiment focused on efficient key management.The contributions of this thesis are of two ways: development of evaluation frameworks to measure scalability and resilience of data sharing methods; and the provision of information on relationships between file sizes and share policies combinations. While the first aimed at providing platform to measure scalability from the point of continuous production as file size and volume increase, and resilience as the potential to continue operation despite cloud outages; the second provides experimental frameworks on the effects of file sizes and share policies on overall system performance.The results of evaluation of FSSS with similar methods showed that the fragmentation method has less overhead costs irrespective of file sizes and the share policy combination. That the inherent challenges in secret sharing scheme can only be solved through alternative means such as combining secret sharing with other data fragmentation method. In all, the system is less of any erasure coding technique, making it difficult to detect corrupt or lost fragment during file recovery