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

An Improved Type System for a Privacy-aware Programming Language and its Practical Applications

Privaatseid andmeid on tarvis analüüsida või töödelda mitmes valdkonnas, näiteks tehes poliitilisi otsusi kasutades riiklikke andmekogusid või pakkudes pilvepõhiseid teenuseid. Sharemind on raamistik turvalisust säilitavate rakenduste arendamiseks, mis võimaldab andmeid analüüsida ilma üksikuid väärtuseid avaldamata. Sharemind kasutab selleks turvalise ühisarvutuse tehnoloogiat. Sharemindi raamistikku kasutavad programmid on kirjutatud programmeerimiskeeles nimega SecreC. Sharemind ja SecreC toetavad erinevaid turvalise ühisarvutuse meetodeid, mida nimetame turvaaladeks. Erinevatel turvaaladel on erinevad turvagarantiid ja efektiivsus ning turvaala valik sõltub konkreetse rakenduse vajadustest, mistõttu peaks SecreC toetama erinevate turvaalade kasutamist vastavalt rakenduse nõuetele. Töö eesmärk on võimaldada SecreC keelele turvaalade lisamist lubades programmeerijal kirjeldada turvaala andmetüübid, aritmeetilised tehted ja tüübiteisendused SecreC keeles. Töö autor lõi keele täiendustele formaalselt kirjeldatud tüübisüsteemi, teostas muudatused SecreC kompilaatoris, kirjeldas muudatuste praktilisi rakendusi, tekkivaid uusi probleeme ja nende võimalikke lahendusi.Confidential data needs to be processed in many areas, for example when making policy decisions using goverment databases or when providing cloud-based services. Sharemind is a framework for developing privacy-preserving applications which allows data to be analysed without revealing individual values. Sharemind uses a technology called secure multi-party computation. Programs using the Sharemind framework are written in a programming language called SecreC. Sharemind and SecreC are designed to support multiple secure multi-party computation methods which we call protection domain kinds. Different protection domain kinds have different security guarantees and performance characteristics and the decision about which one to use depends on the problem at hand which means SecreC should support different protection domain kinds that solve the needs of different applications. The goal of this thesis is to make it easier to add protection domain kinds to the SecreC language by allowing the programmer to define the protection domain kind data types, arithmetic operations and type conversions in the SecreC language without changing the compiler. The author developed a formal type system for the proposed language extensions, implemented them in the SecreC language compiler, described practical applications, open problems and proposed solutions

Privacy-preserving Data Sharing on Vertically Partitioned Data

In this work, we introduce a differentially private method for generating synthetic data from vertically partitioned data, \emph{i.e.}, where data of the same individuals is distributed across multiple data holders or parties. We present a differentially privacy stochastic gradient descent (DP-SGD) algorithm to train a mixture model over such partitioned data using variational inference. We modify a secure multiparty computation (MPC) framework to combine MPC with differential privacy (DP), in order to use differentially private MPC effectively to learn a probabilistic generative model under DP on such vertically partitioned data. Assuming the mixture components contain no dependencies across different parties, the objective function can be factorized into a sum of products of the contributions calculated by the parties. Finally, MPC is used to compute the aggregate between the different contributions. Moreover, we rigorously define the privacy guarantees with respect to the different players in the system. To demonstrate the accuracy of our method, we run our algorithm on the Adult dataset from the UCI machine learning repository, where we obtain comparable results to the non-partitioned case

Privacy-preserving Data clustering in Cloud Computing based on Fully Homomorphic Encryption

Cloud infrastructure with its massive storage and computing power is an ideal platform to perform large scale data analysis tasks to extract knowledge and support decision-making. However, there are critical data privacy and security issues associated with this platform, as the data is stored in a public infrastructure. Recently, fully homomorphic data encryption has been proposed as a solution due to its capabilities in performing computations over encrypted data. However, it is demonstrably slow for practical data mining applications. To address this and related concerns, we introduce a fully homomorphic and distributed data processing framework that utilizes MapReduce to perform distributed computations for data clustering tasks on a large number of cloud Virtual Machines (VMs). We illustrate how a variety of fully homomorphic-based computations can be carried out to accomplish data clustering tasks independently in the cloud and show that the distributed execution of data clustering tasks based on MapReduce can significantly reduce the execution time overhead caused by fully homomorphic computations. To evaluate our framework, we performed experiments using electricity consumption measurement data on the Google cloud platform with 100 VMs. We found the proposed distributed data processing framework to be highly efficient when compared to a centralized approach and as accurate as a plaintext implementation

PUMA: Secure Inference of LLaMA-7B in Five Minutes

With ChatGPT as a representative, tons of companies have began to provide services based on large Transformers models. However, using such a service inevitably leak users' prompts to the model provider. Previous studies have studied secure inference for Transformer models using secure multiparty computation (MPC), where model parameters and clients' prompts are kept secret. Despite this, these frameworks are still limited in terms of model performance, efficiency, and deployment. To address these limitations, we propose framework PUMA to enable fast and secure Transformer model inference. Our framework designs high quality approximations for expensive functions, such as GeLU and Softmax, which significantly reduce the cost of secure inference while preserving the model performance. Additionally, we design secure Embedding and LayerNorm procedures that faithfully implement the desired functionality without undermining the Transformer architecture. PUMA is about 2x faster than the state-of-the-art MPC framework MPCFORMER(ICLR 2023) and has similar accuracy as plaintext models without fine-tuning (which the previous works failed to achieve). One more thing, PUMA can evaluate LLaMA-7B in around 5 minutes to generate 1 token. To our best knowledge, this is the first time that a model with such a parameter size is able to be evaluated under MPC. PUMA has been open-sourced in the Github repository of SecretFlow-SPU

Efficient secure comparison in the dishonest majority model

Secure comparison (SC) is an essential primitive in Secure Multiparty Computation (SMC) and a fundamental building block in Privacy-Preserving Data Analytics (PPDA). Although secure comparison has been studied since the introduction of SMC in the early 80s and many protocols have been proposed, there is still room for improvement, especially providing security against malicious adversaries who form the majority among the participating parties. It is not hard to develop an SC protocol secure against malicious majority based on the current state-of-the-art SPDZ framework. SPDZ is designed to work for arbitrary polynomially-bounded functionalities; it may not provide the most efficient SMC implementation for a specific task, such as SC. In this thesis, we propose a novel and efficient compiler specifically designed to convert most existing SC protocols with semi-honest security into the ones secure against the dishonest majority (malicious majority). We analyze the security of the proposed solutions using the real-ideal paradigm. Moreover, we provide computation and communication complexity analysis. Comparing to the current state-of-the-art SC protocols Rabbit and edaBits, our design offers significant performance gain. The empirical results show that the proposed solution is at least 5 and 10 times more efficient than Rabbit in run-time and communication cost respectively.Includes bibliographical references