Secure data sharing in cloud and IoT by leveraging attribute-based encryption and blockchain

“Data sharing is very important to enable different types of cloud and IoT-based services. For example, organizations migrate their data to the cloud and share it with employees and customers in order to enjoy better fault-tolerance, high-availability, and scalability offered by the cloud. Wearable devices such as smart watch share user’s activity, location, and health data (e.g., heart rate, ECG) with the service provider for smart analytic. However, data can be sensitive, and the cloud and IoT service providers cannot be fully trusted with maintaining the security, privacy, and confidentiality of the data. Hence, new schemes and protocols are required to enable secure data sharing in the cloud and IoT. This work outlines our research contribution towards secure data sharing in the cloud and IoT. For secure data sharing in the cloud, this work proposes several novel attribute-based encryption schemes. The core contributions to this end are efficient revocation, prevention of collusion attacks, and multi-group support. On the other hand, for secure data sharing in IoT, a permissioned blockchain-based access control system has been proposed. The system can be used to enforce fine-grained access control on IoT data where the access control decision is made by the blockchain-based on the consensus of the participating nodes”--Abstract, page iv

Post-Quantum Era Privacy Protection for Intelligent Infrastructures

As we move into a new decade, the global world of Intelligent Infrastructure (II) services integrated into the Internet of Things (IoT) are at the forefront of technological advancements. With billions of connected devices spanning continents through interconnected networks, security and privacy protection techniques for the emerging II services become a paramount concern. In this paper, an up-to-date privacy method mapping and relevant use cases are surveyed for II services. Particularly, we emphasize on post-quantum cryptography techniques that may (or must when quantum computers become a reality) be used in the future through concrete products, pilots, and projects. The topics presented in this paper are of utmost importance as (1) several recent regulations such as Europe's General Data Protection Regulation (GDPR) have given privacy a significant place in digital society, and (2) the increase of IoT/II applications and digital services with growing data collection capabilities are introducing new threats and risks on citizens' privacy. This in-depth survey begins with an overview of security and privacy threats in IoT/IIs. Next, we summarize some selected Privacy-Enhancing Technologies (PETs) suitable for privacy-concerned II services, and then map recent PET schemes based on post-quantum cryptographic primitives which are capable of withstanding quantum computing attacks. This paper also overviews how PETs can be deployed in practical use cases in the scope of IoT/IIs, and maps some current projects, pilots, and products that deal with PETs. A practical case study on the Internet of Vehicles (IoV) is presented to demonstrate how PETs can be applied in reality. Finally, we discuss the main challenges with respect to current PETs and highlight some future directions for developing their post-quantum counterparts

Security and Privacy in the Internet of Things

The Internet of Things (IoT) is an emerging paradigm that seamlessly integrates electronic devices with sensing and computing capability into the Internet to achieve intelligent processing and optimized controlling. In a connected world built through IoT, where interconnected devices are extending to every facet of our lives, including our homes, offices, utility infrastructures and even our bodies, we are able to do things in a way that we never before imagined. However, as IoT redefines the possibilities in environment, society and economy, creating tremendous benefits, significant security and privacy concerns arise such as personal information confidentiality, and secure communication and computation. Theoretically, when everything is connected, everything is at risk. The ubiquity of connected things gives adversaries more attack vectors and more possibilities, and thus more catastrophic consequences by cybercrimes. Therefore, it is very critical to move fast to address these rising security and privacy concerns in IoT systems before severe disasters happen. In this dissertation, we mainly address the challenges in two domains: (1) how to protect IoT devices against cyberattacks; (2) how to protect sensitive data during storage, dissemination and utilization for IoT applications. In the first part, we present how to leverage anonymous communication techniques, particularly Tor, to protect the security of IoT devices. We first propose two schemes to enhance the security of smart home by integrating Tor hidden services into IoT gateway for users with performance preference. Then, we propose a multipath-routing based architecture for Tor hidden services to enhance its resistance against traffic analysis attacks, and thus improving the protection for smart home users who desire very strong security but care less about performance. In the second part of this dissertation, we explore the solutions to protect the data for IoT applications. First, we present a reliable, searchable and privacy-preserving e-healthcare system, which takes advantage of emerging cloud storage and IoT infrastructure and enables healthcare service providers (HSPs) to realize remote patient monitoring in a secure and regulatory compliant manner. Then, we turn our attention to the data analysis in IoT applications, which is one of the core components of IoT applications. We propose a cloud-assisted, privacy-preserving machine learning classification scheme over encrypted data for IoT devices. Our scheme is based on a three-party model coupled with a two-stage decryption Paillier-based cryptosystem, which allows a cloud server to interact with machine learning service providers (MLSPs) and conduct computation intensive classification on behalf of the resourced-constrained IoT devices in a privacy-preserving manner. Finally, we explore the problem of privacy-preserving targeted broadcast in IoT, and propose two multi-cloud-based outsourced-ABE (attribute-based encryption) schemes. They enable the receivers to partially outsource the computationally expensive decryption operations to the clouds, while preventing attributes from being disclosed

Searchable Encryption for Cloud and Distributed Systems

The vast development in information and communication technologies has spawned many new computing and storage architectures in the last two decades. Famous for its powerful computation ability and massive storage capacity, cloud services, including storage and computing, replace personal computers and software systems in many industrial applications. Another famous and influential computing and storage architecture is the distributed system, which refers to an array of machines or components geographically dispersed but jointly contributes to a common task, bringing premium scalability, reliability, and efficiency. Recently, the distributed cloud concept has also been proposed to benefit both cloud and distributed computing. Despite the benefits of these new technologies, data security and privacy are among the main concerns that hinder the wide adoption of these attractive architectures since data and computation are not under the control of the end-users in such systems. The traditional security mechanisms, e.g., encryption, cannot fit these new architectures since they would disable the fast access and retrieval of remote storage servers. Thus, an urgent question turns to be how to enable refined and efficient data retrieval on encrypted data among numerous records (i.e., searchable encryption) in the cloud and distributed systems, which forms the topic of this thesis. Searchable encryption technologies can be divided into Searchable Symmetric Encryption (SSE) and Public-key Encryption with Keyword Search (PEKS). The intrinsical symmetric key hinders data sharing since it is problematic and insecure to reveal one’s key to others. However, SSE outperforms PEKS due to its premium efficiency and is thus is prefered in a number of keyword search applications. Then multi-user SSE with rigorous and fine access control undoubtedly renders a satisfactory solution of both efficiency and security, which is the first problem worthy of our much attention. Second, functions and versatility play an essential role in a cloud storage application but it is still tricky to realize keyword search and deduplication in the cloud simultaneously. Large-scale data usually renders significant data redundancy and saving cloud storage resources turns to be inevitable. Existing schemes only facilitate data retrieval due to keywords but rarely consider other demands like deduplication. To be noted, trivially and hastily affiliating a separate deduplication scheme to the searchable encryption leads to disordered system architecture and security threats. Therefore, attention should be paid to versatile solutions supporting both keyword search and deduplication in the cloud. The third problem to be addressed is implementing multi-reader access for PEKS. As we know, PEKS was born to support multi-writers but enabling multi-readers in PEKS is challenging. Repeatedly encrypting the same keyword with different readers’ keys is not an elegant solution. In addition to keyword privacy, user anonymity coming with a multi-reader setting should also be formulated and preserved. Last but not least, existing schemes targeting centralized storage have not taken full advantage of distributed computation, which is considerable efficiency and fast response. Specifically, all testing tasks between searchable ciphertexts and trapdoor/token are fully undertaken by the only centralized cloud server, resulting in a busy system and slow response. With the help of distributed techniques, we may now look forward to a new turnaround, i.e., multiple servers jointly work to perform the testing with better efficiency and scalability. Then the intractable multi-writer/multi-reader mode supporting multi-keyword queries may also come true as a by-product. This thesis investigates searchable encryption technologies in cloud storage and distributed systems and spares effort to address the problems mentioned above. Our first work can be classified into SSE. We formulate the Multi-user Verifiable Searchable Symmetric Encryption (MVSSE) and propose a concrete scheme for multi-user access. It not only offers multi-user access and verifiability but also supports extension on updates as well as a non-single keyword index. Moreover, revocable access control is obtained that the search authority is validated each time a query is launched, different from existing mechanisms that once the search authority is granted, users can search forever. We give simulation-based proof, demonstrating our proposal possesses Universally Composable (UC)-security. Second, we come up with a redundancy elimination solution on top of searchable encryption. Following the keyword comparison approach of SSE, we formulate a hybrid primitive called Message-Locked Searchable Encryption (MLSE) derived in the way of SSE’s keyword search supporting keyword search and deduplication and present a concrete construction that enables multi-keyword query and negative keyword query as well as deduplication at a considerable small cost, i.e., the tokens are used for both search and deduplication. And it can further support Proof of Storage (PoS), testifying the content integrity in cloud storage. The semantic security is proved in Random Oracle Model using the game-based methodology. Third, as the branch of PEKS, the Broadcast Authenticated Encryption with Keyword Search (BAEKS) is proposed to bridge the gap of multi-reader access for PEKS, followed by a scheme. It not only resists Keyword Guessing Attacks (KGA) but also fills in the blank of anonymity. The scheme is proved secure under Decisional Bilinear Diffie-Hellman (DBDH) assumption in the Random Oracle Model. For distributed systems, we present a Searchable Encryption based on Efficient Privacy-preserving Outsourced calculation framework with Multiple keys (SE-EPOM) enjoying desirable features, which can be classified into PEKS. Instead of merely deploying a single server, multiple servers are employed to execute the test algorithm in our scheme jointly. The refined search, i.e., multi-keyword query, data confidentiality, and search pattern hiding, are realized. Besides, the multi-writer/multi-reader mode comes true. It is shown that under the distributed circumstance, much efficiency can be substantially achieved by our construction. With simulation-based proof, the security of our scheme is elaborated. All constructions proposed in this thesis are formally proven according to their corresponding security definitions and requirements. In addition, for each cryptographic primitive designed in this thesis, concrete schemes are initiated to demonstrate the availability and practicality of our proposal

Practical yet Provably Secure: Complex Database Query Execution over Encrypted Data

Encrypted databases provide security for outsourced data. In this work novel encryption schemes supporting different database query types are presented enabling complex database queries over encrypted data. For specific constructions enabling exact keyword queries, range queries, database joins and substring queries over encrypted data we prove security in a formal framework, present a theoretical runtime analysis and provide an assessment of practical performance characteristics

Secure Protocols for Privacy-preserving Data Outsourcing, Integration, and Auditing

As the amount of data available from a wide range of domains has increased tremendously in recent years, the demand for data sharing and integration has also risen. The cloud computing paradigm provides great flexibility to data owners with respect to computation and storage capabilities, which makes it a suitable platform for them to share their data. Outsourcing person-specific data to the cloud, however, imposes serious concerns about the confidentiality of the outsourced data, the privacy of the individuals referenced in the data, as well as the confidentiality of the queries processed over the data. Data integration is another form of data sharing, where data owners jointly perform the integration process, and the resulting dataset is shared between them. Integrating related data from different sources enables individuals, businesses, organizations and government agencies to perform better data analysis, make better informed decisions, and provide better services. Designing distributed, secure, and privacy-preserving protocols for integrating person-specific data, however, poses several challenges, including how to prevent each party from inferring sensitive information about individuals during the execution of the protocol, how to guarantee an effective level of privacy on the released data while maintaining utility for data mining, and how to support public auditing such that anyone at any time can verify that the integration was executed correctly and no participants deviated from the protocol. In this thesis, we address the aforementioned concerns by presenting secure protocols for privacy-preserving data outsourcing, integration and auditing. First, we propose a secure cloud-based data outsourcing and query processing framework that simultaneously preserves the confidentiality of the data and the query requests, while providing differential privacy guarantees on the query results. Second, we propose a publicly verifiable protocol for integrating person-specific data from multiple data owners, while providing differential privacy guarantees and maintaining an effective level of utility on the released data for the purpose of data mining. Next, we propose a privacy-preserving multi-party protocol for high-dimensional data mashup with guaranteed LKC-privacy on the output data. Finally, we apply the theory to the real world problem of solvency in Bitcoin. More specifically, we propose a privacy-preserving and publicly verifiable cryptographic proof of solvency scheme for Bitcoin exchanges such that no information is revealed about the exchange's customer holdings, the value of the exchange's total holdings is kept secret, and multiple exchanges performing the same proof of solvency can contemporaneously prove they are not colluding

Novel Techniques for Secure Use of Public Cloud Computing Resources

The federal government has an expressed interest in moving data and services to third party service providers in order to take advantage of the flexibility, scalability, and potential cost savings. This approach is called cloud computing. The thesis for this research is that efficient techniques exist to support the secure use of public cloud computing resources by a large, federated enterprise. The primary contributions of this research are the novel cryptographic system MA-AHASBE (Multi-Authority Anonymous Hierarchical Attribute-Set Based Encryption), and the techniques used to incorporate MA-AHASBE in a real world application. Performance results indicate that while there is a cost associated with enforcing the suggested security model, the cost is not unreasonable and the benefits in security can be significant. The contributions of this research give the DoD additional tools for supporting the mission while taking advantage of the cost efficient public cloud computing resources that are becoming widely available

NEW SECURE SOLUTIONS FOR PRIVACY AND ACCESS CONTROL IN HEALTH INFORMATION EXCHANGE

In the current digital age, almost every healthcare organization (HCO) has moved from storing patient health records on paper to storing them electronically. Health Information Exchange (HIE) is the ability to share (or transfer) patients’ health information between different HCOs while maintaining national security standards like the Health Insurance Portability and Accountability Act (HIPAA) of 1996. Over the past few years, research has been conducted to develop privacy and access control frameworks for HIE systems. The goal of this dissertation is to address the privacy and access control concerns by building practical and efficient HIE frameworks to secure the sharing of patients’ health information. The first solution allows secure HIE among different healthcare providers while focusing primarily on the privacy of patients’ information. It allows patients to authorize a certain type of health information to be retrieved, which helps prevent any unintentional leakage of information. The privacy solution also provides healthcare providers with the capability of mutual authentication and patient authentication. It also ensures the integrity and auditability of health information being exchanged. The security and performance study for the first protocol shows that it is efficient for the purpose of HIE and offers a high level of security for such exchanges. The second framework presents a new cloud-based protocol for access control to facilitate HIE across different HCOs, employing a trapdoor hash-based proxy signature in a novel manner to enable secure (authenticated and authorized) on-demand access to patient records. The proposed proxy signature-based scheme provides an explicit mechanism for patients to authorize the sharing of specific medical information with specific HCOs, which helps prevent any undesired or unintentional leakage of health information. The scheme also ensures that such authorizations are authentic with respect to both the HCOs and the patient. Moreover, the use of proxy signatures simplifies security auditing and the ability to obtain support for investigations by providing non-repudiation. Formal definitions, security specifications, and a detailed theoretical analysis, including correctness, security, and performance of both frameworks are provided which demonstrate the improvements upon other existing HIE systems