Multi-precision arithmetic on a DSP

The aim of this project has been to develop the assembly language functions needed to allow easy implementation in real-time of a secure speech channel. The theory of security systems is introduced and developed. Encryption algorithms are described. A library of multi-precision arithmetic routines has been written for use on the TMS320C25 digital signed processor. These routines are compatible with code produced by the TMS320C25 C Compiler. Multi-precision arithmetic is used in public key encryption which requires large number arithmetic for security and which also has real-time operation requirements. An overview of DSP use in this kind of application is given, the design, implementation and test of these routines is described and some application examples and timings are shown

Secure Arithmetic Computation with Constant Computational Overhead

We study the complexity of securely evaluating an arithmetic circuit over a finite field $F$ in the setting of secure two-party computation with semi-honest adversaries. In all existing protocols, the number of arithmetic operations per multiplication gate grows either linearly with $\log |F|$ or polylogarithmically with the security parameter. We present the first protocol that only makes a *constant* (amortized) number of field operations per gate. The protocol uses the underlying field $F$ as a black box, and its security is based on arithmetic analogues of well-studied cryptographic assumptions. Our protocol is particularly appealing in the special case of securely evaluating a ``vector-OLE\u27\u27 function of the form $\vec{a}x+\vec{b}$, where $x\in F$ is the input of one party and $\vec{a},\vec{b}\in F^w$ are the inputs of the other party. In this case, which is motivated by natural applications, our protocol can achieve an asymptotic rate of $1/3$ (i.e., the communication is dominated by sending roughly $3w$ elements of $F$). Our implementation of this protocol suggests that it outperforms competing approaches even for relatively small fields $F$ and over fast networks. Our technical approach employs two new ingredients that may be of independent interest. First, we present a general way to combine any linear code that has a fast encoder and a cryptographic (``LPN-style\u27\u27) pseudorandomness property with another linear code that supports fast encoding and *erasure-decoding*, obtaining a code that inherits both the pseudorandomness feature of the former code and the efficiency features of the latter code. Second, we employ local *arithmetic* pseudo-random generators, proposing arithmetic generalizations of boolean candidates that resist all known attacks

ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange

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

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