Enabling Secure Database as a Service using Fully Homomorphic Encryption: Challenges and Opportunities

The database community, at least for the last decade, has been grappling with querying encrypted data, which would enable secure database as a service solutions. A recent breakthrough in the cryptographic community (in 2009) related to fully homomorphic encryption (FHE) showed that arbitrary computation on encrypted data is possible. Successful adoption of FHE for query processing is, however, still a distant dream, and numerous challenges have to be addressed. One challenge is how to perform algebraic query processing of encrypted data, where we produce encrypted intermediate results and operations on encrypted data can be composed. In this paper, we describe our solution for algebraic query processing of encrypted data, and also outline several other challenges that need to be addressed, while also describing the lessons that can be learnt from a decade of work by the database community in querying encrypted data

CryptGraph: Privacy Preserving Graph Analytics on Encrypted Graph

Many graph mining and analysis services have been deployed on the cloud, which can alleviate users from the burden of implementing and maintaining graph algorithms. However, putting graph analytics on the cloud can invade users' privacy. To solve this problem, we propose CryptGraph, which runs graph analytics on encrypted graph to preserve the privacy of both users' graph data and the analytic results. In CryptGraph, users encrypt their graphs before uploading them to the cloud. The cloud runs graph analysis on the encrypted graphs and obtains results which are also in encrypted form that the cloud cannot decipher. During the process of computing, the encrypted graphs are never decrypted on the cloud side. The encrypted results are sent back to users and users perform the decryption to obtain the plaintext results. In this process, users' graphs and the analytics results are both encrypted and the cloud knows neither of them. Thereby, users' privacy can be strongly protected. Meanwhile, with the help of homomorphic encryption, the results analyzed from the encrypted graphs are guaranteed to be correct. In this paper, we present how to encrypt a graph using homomorphic encryption and how to query the structure of an encrypted graph by computing polynomials. To solve the problem that certain operations are not executable on encrypted graphs, we propose hard computation outsourcing to seek help from users. Using two graph algorithms as examples, we show how to apply our methods to perform analytics on encrypted graphs. Experiments on two datasets demonstrate the correctness and feasibility of our methods

Chaotic Compilation for Encrypted Computing: Obfuscation but Not in Name

An `obfuscation' for encrypted computing is quantified exactly here, leading to an argument that security against polynomial-time attacks has been achieved for user data via the deliberately `chaotic' compilation required for security properties in that environment. Encrypted computing is the emerging science and technology of processors that take encrypted inputs to encrypted outputs via encrypted intermediate values (at nearly conventional speeds). The aim is to make user data in general-purpose computing secure against the operator and operating system as potential adversaries. A stumbling block has always been that memory addresses are data and good encryption means the encrypted value varies randomly, and that makes hitting any target in memory problematic without address decryption, yet decryption anywhere on the memory path would open up many easily exploitable vulnerabilities. This paper `solves (chaotic) compilation' for processors without address decryption, covering all of ANSI C while satisfying the required security properties and opening up the field for the standard software tool-chain and infrastructure. That produces the argument referred to above, which may also hold without encryption.Comment: 31 pages. Version update adds "Chaotic" in title and throughout paper, and recasts abstract and Intro and other sections of the text for better access by cryptologists. To the same end it introduces the polynomial time defense argument explicitly in the final section, having now set that denouement out in the abstract and intr

Detecting Key-Dependencies

The confidentiality of encrypted data depends on how well the key under which it was encrypted is maintained. If a session key was exchanged encrypted under a long-term key, exposure of the long-term key may reveal the session key and hence the data encrypted with it. The problem of key-dependencies between keys can be mapped onto connectivity of a graph, and the resulting graph can be inspected. This article presents a structured method (an algorithm) with which key-dependencies can be detected and analysed. Several well-known protocols are examined, and it is shown that they are vulnerable to certain attacks exploiting key-dependencies. Protocols which are free from this defect do exist. That is, when a session is terminated it is properly closed

A First Practical Fully Homomorphic Crypto-Processor Design: The Secret Computer is Nearly Here

Following a sequence of hardware designs for a fully homomorphic crypto-processor - a general purpose processor that natively runs encrypted machine code on encrypted data in registers and memory, resulting in encrypted machine states - proposed by the authors in 2014, we discuss a working prototype of the first of those, a so-called `pseudo-homomorphic' design. This processor is in principle safe against physical or software-based attacks by the owner/operator of the processor on user processes running in it. The processor is intended as a more secure option for those emerging computing paradigms that require trust to be placed in computations carried out in remote locations or overseen by untrusted operators. The prototype has a single-pipeline superscalar architecture that runs OpenRISC standard machine code in two distinct modes. The processor runs in the encrypted mode (the unprivileged, `user' mode, with a long pipeline) at 60-70% of the speed in the unencrypted mode (the privileged, `supervisor' mode, with a short pipeline), emitting a completed encrypted instruction every 1.67-1.8 cycles on average in real trials.Comment: 6 pages, draf