An efficient framework for privacy-preserving computations on encrypted IoT data

There are two fundamental expectations from Cloud-IoT applications using sensitive and personal data: data utility and user privacy. With the complex nature of cloud-IoT ecosystem, there is a growing concern about data utility at the cost of privacy. While the current state-of-the-art encryption schemes protect users’ privacy, they preclude meaningful computations on encrypted data. Thus, the question remains “how to help IoT device users benefit from cloud computing without compromising data confidentiality and user privacy”? Cloud service providers (CSP) can leverage Fully homomorphic encryption (FHE) schemes to deliver privacy-preserving services. However, there are limitations in directly adopting FHE-based solutions for real-world Cloud-IoT applications. Thus, to foster real-world adoption of FHE-based solutions, we propose a framework called Proxy re-ciphering as a service. It leverages existing schemes such as distributed proxy servers, threshold secret sharing, chameleon hash function and FHE to tailor a practical solution that enables long-term privacy-preserving cloud computations for IoT ecosystem. We also encourage CSPs to store minimal yet adequate information from processing the raw IoT device data. Furthermore, we explore a way for IoT devices to refresh their device keys after a key-compromise. To evaluate the framework, we first develop a testbed and measure the latencies with real-world ECG records from TELE ECG Database. We observe that i) although the distributed framework introduces computation and communication latencies, the security gains outweighs the latencies, ii) the throughput of the servers providing re-ciphering service can be greatly increased with pre-processing iii) with a key refresh scheme we can limit the upper bound on the attack window post a key-compromise. Finally, we analyze the security properties against major threats faced by Cloud-IoT ecosystem. We infer that Proxy re-ciphering as a service is a practical, secure, scalable and an easy-to-adopt framework for long-term privacy-preserving cloud computations for encrypted IoT data

A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The concept of cloud computing offers measurable computational or information resources as a service over the Internet. The major motivation behind the cloud setup is economic benefits, because it assures the reduction in expenditure for operational and infrastructural purposes. To transform it into a reality there are some impediments and hurdles which are required to be tackled, most profound of which are security, privacy and reliability issues. As the user data is revealed to the cloud, it departs the protection-sphere of the data owner. However, this brings partly new security and privacy concerns. This work focuses on these issues related to various cloud services and deployment models by spotlighting their major challenges. While the classical cryptography is an ancient discipline, modern cryptography, which has been mostly developed in the last few decades, is the subject of study which needs to be implemented so as to ensure strong security and privacy mechanisms in today’s real-world scenarios. The technological solutions, short and long term research goals of the cloud security will be described and addressed using various classical cryptographic mechanisms as well as modern ones. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE

Malware Analysis with Machine Learning

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2022Malware attacks have been one of the most serious cyber risks in recent years. Almost every week, the number of vulnerability reports is increasing in the security communities. One of the key causes for the exponential growth is the fact that malware authors started introducing mutations to avoid detection. This means that malicious files from the same malware family, with the same malicious behaviour, are constantly modified or obfuscated using a variety of technics to make them appear to be different. Characteristics retrieved from raw binary files or disassembled code are used in existing machine learning-based malware categorization algorithms. The variety of such attributes has made it difficult to develop generic malware categorization methods that operate well in a variety of operating scenarios. To be effective in evaluating and categorizing such enormous volumes of data, it is necessary to divide them into groups and identify their respective families based on their behaviour. Malicious software is converted to a greyscale image representation, due to the possibility to capture subtle changes while keeping the global structure helps to detect variations. Motivated by the Machine Learning results achieved in the ImageNet challenge, this dissertation proposes an agnostic deep learning solution, for efficiently classifying malware into families based on a collection of discriminant patterns retrieved from its visualization as images. In this thesis, we present Malwizard, an adaptable Python solution suited for companies or end users, that allows them to automatically obtain a fast malware analysis. The solution was implemented as an Outlook add-in and an API service for the SOAR platforms, as emails are the first vector for this type of attack, with companies being the most attractive targets. The Microsoft Classification Challenge dataset was used in the evaluation of the noble approach. Therefore, its image representation was ciphered and generated the correspondent ciphered image to evaluate if the same patterns could be identified using traditional machine learning techniques. Thus, allowing the privacy concerns to be addressed, maintaining the data analysed by neural networks secure to unauthorized parties. Experimental comparison demonstrates the noble approach performed close to the best analysed model on a plain text dataset, completing the task in one-third of the time. Regarding the encrypted dataset, classical techniques need to be adapted in order to be efficient

Efficient Computation and FPGA implementation of Fully Homomorphic Encryption with Cloud Computing Significance

Homomorphic Encryption provides unique security solution for cloud computing. It ensures not only that data in cloud have confidentiality but also that data processing by cloud server does not compromise data privacy. The Fully Homomorphic Encryption (FHE) scheme proposed by Lopez-Alt, Tromer, and Vaikuntanathan (LTV), also known as NTRU(Nth degree truncated polynomial ring) based method, is considered one of the most important FHE methods suitable for practical implementation. In this thesis, an efficient algorithm and architecture for LTV Fully Homomorphic Encryption is proposed. Conventional linear feedback shift register (LFSR) structure is expanded and modified for performing the truncated polynomial ring multiplication in LTV scheme in parallel. Novel and efficient modular multiplier, modular adder and modular subtractor are proposed to support high speed processing of LFSR operations. In addition, a family of special moduli are selected for high speed computation of modular operations. Though the area keeps the complexity of O(Nn^2) with no advantage in circuit level. The proposed architecture effectively reduces the time complexity from O(N log N) to linear time, O(N), compared to the best existing works. An FPGA implementation of the proposed architecture for LTV FHE is achieved and demonstrated. An elaborate comparison of the existing methods and the proposed work is presented, which shows the proposed work gains significant speed up over existing works

Securing Cloud Computations with Oblivious Primitives from Intel SGX

We are witnessing a confluence between applied cryptography and secure hardware systems in enabling secure cloud computing. On one hand, work in applied cryptography has enabled efficient, oblivious data-structures and memory primitives. On the other, secure hardware and the emergence of Intel SGX has enabled a low-overhead and mass market mechanism for isolated execution. By themselves these technologies have their disadvantages. Oblivious memory primitives carry high performance overheads, especially when run non-interactively. Intel SGX, while more efficient, suffers from numerous software-based side-channel attacks, high context switching costs, and bounded memory size. In this work we build a new library of oblivious memory primitives, which we call ZeroTrace. ZeroTrace is designed to carefully combine state-of-art oblivious RAM techniques and SGX, while mitigating individual disadvantages of these technologies. To the best of our knowledge, ZeroTrace represents the first oblivious memory primitives running on a real secure hardware platform. ZeroTrace simultaneously enables a dramatic speedup over pure cryptography and protection from software-based side-channel attacks. The core of our design is an efficient and flexible block-level memory controller that provides oblivious execution against any active software adversary, and across asynchronous SGX enclave terminations. Performance-wise, the memory controller can service requests for 4~Byte blocks in 1.2~ms and 1~KB blocks in 3.4~ms (given a 10~GB dataset). On top of our memory controller, we evaluate Set/Dictionary/List interfaces which can all perform basic operations (e.g., get/put/insert) in 1-5~ms for a 4-8~Byte block size. ZeroTrace enables secure remote computations at substantially lower overheads than other comparable state-of-the-art techniques

CryptDB: Protecting confidentiality with encrypted query processing

Online applications are vulnerable to theft of sensitive information because adversaries can exploit software bugs to gain access to private data, and because curious or malicious administrators may capture and leak data. CryptDB is a system that provides practical and provable confidentiality in the face of these attacks for applications backed by SQL databases. It works by executing SQL queries over encrypted data using a collection of efficient SQL-aware encryption schemes. CryptDB can also chain encryption keys to user passwords, so that a data item can be decrypted only by using the password of one of the users with access to that data. As a result, a database administrator never gets access to decrypted data, and even if all servers are compromised, an adversary cannot decrypt the data of any user who is not logged in. An analysis of a trace of 126 million SQL queries from a production MySQL server shows that CryptDB can support operations over encrypted data for 99.5% of the 128,840 columns seen in the trace. Our evaluation shows that CryptDB has low overhead, reducing throughput by 14.5% for phpBB, a web forum application, and by 26% for queries from TPC-C, compared to unmodified MySQL. Chaining encryption keys to user passwords requires 11--13 unique schema annotations to secure more than 20 sensitive fields and 2--7 lines of source code changes for three multi-user web applications.National Science Foundation (U.S.) (CNS-0716273)National Science Foundation (U.S.) (IIS-1065219

Private Join and Compute from PIR with Default

The private join and compute (PJC) functionality enables secure computation over data distributed across different databases, which is a functionality with a wide range of applications, many of which address settings where the input databases are of significantly different sizes. We introduce the notion of private information retrieval (PIR) with default, which enables two-party PJC functionalities in a way that hides the size of the intersection of the two databases and incurs sublinear communication cost in the size of the bigger database. We provide two constructions for this functionality, one of which requires offline linear communication, which can be amortized across queries, and one that provides sublinear cost for each query but relies on more computationally expensive tools. We construct inner-product PJC, which has applications to ads conversion measurement and contact tracing, relying on an extension of PIR with default. We evaluate the efficiency of our constructions, which can enable $\mathbf{2^{12}}$ PIR with default lookups on a database of size $\mathbf{2^{30}}$ (or inner-product PJC on databases with such sizes) with the communication of $\mathbf{945}$MB, which costs less than $\mathbf{\$0.04}$ for the client and $\mathbf{\$5.22}$ for the server

Privacy-preserving efficient searchable encryption

Data storage and computation outsourcing to third-party managed data centers, in environments such as Cloud Computing, is increasingly being adopted by individuals, organizations, and governments. However, as cloud-based outsourcing models expand to society-critical data and services, the lack of effective and independent control over security and privacy conditions in such settings presents significant challenges. An interesting solution to these issues is to perform computations on encrypted data, directly in the outsourcing servers. Such an approach benefits from not requiring major data transfers and decryptions, increasing performance and scalability of operations. Searching operations, an important application case when cloud-backed repositories increase in number and size, are good examples where security, efficiency, and precision are relevant requisites. Yet existing proposals for searching encrypted data are still limited from multiple perspectives, including usability, query expressiveness, and client-side performance and scalability. This thesis focuses on the design and evaluation of mechanisms for searching encrypted data with improved efficiency, scalability, and usability. There are two particular concerns addressed in the thesis: on one hand, the thesis aims at supporting multiple media formats, especially text, images, and multimodal data (i.e. data with multiple media formats simultaneously); on the other hand the thesis addresses client-side overhead, and how it can be minimized in order to support client applications executing in both high-performance desktop devices and resource-constrained mobile devices. From the research performed to address these issues, three core contributions were developed and are presented in the thesis: (i) CloudCryptoSearch, a middleware system for storing and searching text documents with privacy guarantees, while supporting multiple modes of deployment (user device, local proxy, or computational cloud) and exploring different tradeoffs between security, usability, and performance; (ii) a novel framework for efficiently searching encrypted images based on IES-CBIR, an Image Encryption Scheme with Content-Based Image Retrieval properties that we also propose and evaluate; (iii) MIE, a Multimodal Indexable Encryption distributed middleware that allows storing, sharing, and searching encrypted multimodal data while minimizing client-side overhead and supporting both desktop and mobile devices