A Survey on Homomorphic Encryption Schemes: Theory and Implementation

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars of achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, as well as extending the state of the art HE, PHE, SWHE, and FHE systems.Comment: - Updated. (October 6, 2017) - This paper is an early draft of the survey that is being submitted to ACM CSUR and has been uploaded to arXiv for feedback from stakeholder

Practical Isolated Searchable Encryption in a Trusted Computing Environment

Cloud computing has become a standard computational paradigm due its numerous advantages, including high availability, elasticity, and ubiquity. Both individual users and companies are adopting more of its services, but not without loss of privacy and control. Outsourcing data and computations to a remote server implies trusting its owners, a problem many end-users are aware. Recent news have proven data stored on Cloud servers is susceptible to leaks from the provider, third-party attackers, or even from government surveillance programs, exposing users’ private data. Different approaches to tackle these problems have surfaced throughout the years. Naïve solutions involve storing data encrypted on the server, decrypting it only on the client-side. Yet, this imposes a high overhead on the client, rendering such schemes impractical. Searchable Symmetric Encryption (SSE) has emerged as a novel research topic in recent years, allowing efficient querying and updating over encrypted datastores in Cloud servers, while retaining privacy guarantees. Still, despite relevant recent advances, existing SSE schemes still make a critical trade-off between efficiency, security, and query expressiveness, thus limiting their adoption as a viable technology, particularly in large-scale scenarios. New technologies providing Isolated Execution Environments (IEEs) may help improve SSE literature. These technologies allow applications to be run remotely with privacy guarantees, in isolation from other, possibly privileged, processes inside the CPU, such as the operating system kernel. Prominent example technologies are Intel SGX and ARM TrustZone, which are being made available in today’s commodity CPUs. In this thesis we study these new trusted hardware technologies in depth, while exploring their application to the problem of searching over encrypted data, primarily focusing in SGX. In more detail, we study the application of IEEs in SSE schemes, improving their efficiency, security, and query expressiveness. We design, implement, and evaluate three new SSE schemes for different query types, namely Boolean queries over text, similarity queries over image datastores, and multimodal queries over text and images. These schemes can support queries combining different media formats simultaneously, envisaging applications such as privacy-enhanced medical diagnosis and management of electronic-healthcare records, or confidential photograph catalogues, running without the danger of privacy breaks in Cloud-based provisioned services

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

Privacy-preserving queries on encrypted databases

In today's Internet, with the advent of cloud computing, there is a natural desire for enterprises, organizations, and end users to outsource increasingly large amounts of data to a cloud provider. Therefore, ensuring security and privacy is becoming a significant challenge for cloud computing, especially for users with sensitive and valuable data. Recently, many efficient and scalable query processing methods over encrypted data have been proposed. Despite that, numerous challenges remain to be addressed due to the high complexity of many important queries on encrypted large-scale datasets. This thesis studies the problem of privacy-preserving database query processing on structured data (e.g., relational and graph databases). In particular, this thesis proposes several practical and provable secure structured encryption schemes that allow the data owner to encrypt data without losing the ability to query and retrieve it efficiently for authorized clients. This thesis includes two parts. The first part investigates graph encryption schemes. This thesis proposes a graph encryption scheme for approximate shortest distance queries. Such scheme allows the client to query the shortest distance between two nodes in an encrypted graph securely and efficiently. Moreover, this thesis also explores how the techniques can be applied to other graph queries. The second part of this thesis proposes secure top-k query processing schemes on encrypted relational databases. Furthermore, the thesis develops a scheme for the top-k join queries over multiple encrypted relations. Finally, this thesis demonstrates the practicality of the proposed encryption schemes by prototyping the encryption systems to perform queries on real-world encrypted datasets

Survey of Homomorphic schemes

Homomorphic encryption is increasingly becoming popular among researchers due to its future promises.Homomorphic encryption is a solution that allows a third party to process data in encrypted form. The decryption keys need not be shared.This paper summarizes the concept of homomorphic encryption and the work has been done in this field

Cloud-based homomorphic encryption for privacy-preserving machine learning in clinical decision support

While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that ensures secure processing of sensitive data via untrusted networks in the public cloud or by third-party cloud vendors. It relies on the fact that some encryption algorithms display the property of homomorphism, which allows them to manipulate data meaningfully while still in encrypted form; although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. Such a framework would find particular relevance in Clinical Decision Support (CDS) applications deployed in the public cloud. CDS applications have an important computational and analytical role over confidential healthcare information with the aim of supporting decision-making in clinical practice. Machine Learning (ML) is employed in CDS applications that typically learn and can personalise actions based on individual behaviour. A relatively simple-to-implement, common and consistent framework is sought that can overcome most limitations of Fully Homomorphic Encryption (FHE) in order to offer an expanded and flexible set of HE capabilities. In the absence of a significant breakthrough in FHE efficiency and practical use, it would appear that a solution relying on client interactions is the best known entity for meeting the requirements of private CDS-based computation, so long as security is not significantly compromised. A hybrid solution is introduced, that intersperses limited two-party interactions amongst the main homomorphic computations, allowing exchange of both numerical and logical cryptographic contexts in addition to resolving other major FHE limitations. Interactions involve the use of client-based ciphertext decryptions blinded by data obfuscation techniques, to maintain privacy. This thesis explores the middle ground whereby HE schemes can provide improved and efficient arbitrary computational functionality over a significantly reduced two-party network interaction model involving data obfuscation techniques. This compromise allows for the powerful capabilities of HE to be leveraged, providing a more uniform, flexible and general approach to privacy-preserving system integration, which is suitable for cloud deployment. The proposed platform is uniquely designed to make HE more practical for mainstream clinical application use, equipped with a rich set of capabilities and potentially very complex depth of HE operations. Such a solution would be suitable for the long-term privacy preserving-processing requirements of a cloud-based CDS system, which would typically require complex combinatorial logic, workflow and ML capabilities

Is Homomorphic Encryption Feasible for Smart Mobility?

Smart mobility is a promising approach to meet urban transport needs in an environmentally and and user-friendly way. Smart mobility computes itineraries with multiple means of transportation, e.g., trams, rental bikes or electric scooters, according to customer preferences. A mobility platform cares for reservations, connecting transports, invoicing and billing. This requires sharing sensible personal data with multiple parties, and puts data privacy at risk. In this paper, we investigate if fully homomorphic encryption (FHE) can be applied in practice to mitigate such privacy issues. FHE allows to calculate on encrypted data, without having to decrypt it first. We implemented three typical distributed computations in a smart mobility scenario with SEAL, a recent programming library for FHE. With this implementation, we have measured memory consumption and execution times for three variants of distributed transactions, that are representative for a wide range of smart mobility tasks. Our evaluation shows, that FHE is indeed applicable to smart mobility: With today's processing capabilities, state-of-the-art FHE increases a smart mobility transaction by about 100 milliseconds and less than 3 microcents.Comment: submitted to FedCSI

Security and privacy issues of physical objects in the IoT: Challenges and opportunities

In the Internet of Things (IoT), security and privacy issues of physical objects are crucial to the related applications. In order to clarify the complicated security and privacy issues, the life cycle of a physical object is divided into three stages of pre-working, in-working, and post-working. On this basis, a physical object-based security architecture for the IoT is put forward. According to the security architecture, security and privacy requirements and related protecting technologies for physical objects in different working stages are analyzed in detail. Considering the development of IoT technologies, potential security and privacy challenges that IoT objects may face in the pervasive computing environment are summarized. At the same time, possible directions for dealing with these challenges are also pointed out

Secure Abstractions for Trusted Cloud Computation

Cloud computing is adopted by most organizations due to its characteristics, namely offering on-demand resources and services that can quickly be provisioned with minimal management effort and maintenance expenses for its users. However it still suffers from security incidents which have lead to many data security concerns and reluctance in further adherence. With the advent of these incidents, cryptographic technologies such as homomorphic and searchable encryption schemes were leveraged to provide solutions that mitigated data security concerns. The goal of this thesis is to provide a set of secure abstractions to serve as a tool for programmers to develop their own distributed applications. Furthermore, these abstractions can also be used to support trusted cloud computations in the context of NoSQL data stores. For this purpose we leveraged conflict-free replicated data types (CRDTs) as they provide a mechanism to ensure data consistency when replicated that has no need for synchronization, which aligns well with the distributed and replicated nature of the cloud, and the aforementioned cryptographic technologies to comply with the security requirements. The main challenge of this thesis consisted in combining the cryptographic technologies with the CRDTs in such way that it was possible to support all of the data structures functionalities over ciphertext while striving to attain the best security and performance possible. To evaluate our abstractions we conducted an experiment to compare each secure abstraction with their non secure counterpart performance wise. Additionally, we also analysed the security level provided by each of the structures in light of the cryptographic scheme used to support it. The results of our experiment shows that our abstractions provide the intended data security with an acceptable performance overhead, showing that it has potential to be used to build solutions for trusted cloud computation

An improved Framework for Biometric Database’s privacy

Security and privacy are huge challenges in biometric systems. Biometrics are sensitive data that should be protected from any attacker and especially attackers targeting the confidentiality and integrity of biometric data. In this paper an extensive review of different physiological biometric techniques is provided. A comparative analysis of the various sus mentioned biometrics, including characteristics and properties is conducted. Qualitative and quantitative evaluation of the most relevant physiological biometrics is achieved. Furthermore, we propose a new framework for biometric database privacy. Our approach is based on the use of the promising fully homomorphic encryption technology. As a proof of concept, we establish an initial implementation of our security module using JAVA programming language