Interaction-Preserving Compilers for Secure Computation

In this work we consider the following question: What is the cost of security for multi-party protocols? Specifically, given an insecure protocol where parties exchange (in the worst case) ? bits in N rounds, is it possible to design a secure protocol with communication complexity close to ? and N rounds? We systematically study this problem in a variety of settings and we propose solutions based on the intractability of different cryptographic problems. For the case of two parties we design an interaction-preserving compiler where the number of bits exchanged in the secure protocol approaches ? and the number of rounds is exactly N, assuming the hardness of standard problems over lattices. For the more general multi-party case, we obtain the same result assuming either (i) an additional round of interaction or (ii) the existence of extractable witness encryption and succinct non-interactive arguments of knowledge. As a contribution of independent interest, we construct the first multi-key fully homomorphic encryption scheme with message-to-ciphertext ratio (i.e., rate) of 1 - o(1), assuming the hardness of the learning with errors (LWE) problem. We view our work as a support for the claim that, as far as interaction and communication are concerned, one does not need to pay a significant price for security in multi-party protocols

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

Foundations of Homomorphic Secret Sharing

Homomorphic secret sharing (HSS) is the secret sharing analogue of homomorphic encryption. An HSS scheme supports a local evaluation of functions on shares of one or more secret inputs, such that the resulting shares of the output are short. Some applications require the stronger notion of additive HSS, where the shares of the output add up to the output over some finite Abelian group. While some strong positive results for HSS are known under specific cryptographic assumptions, many natural questions remain open. We initiate a systematic study of HSS, making the following contributions. - A definitional framework. We present a general framework for defining HSS schemes that unifies and extends several previous notions from the literature, and cast known results within this framework. - Limitations. We establish limitations on information-theoretic multi-input HSS with short output shares via a relation with communication complexity. We also show that additive HSS for non-trivial functions, even the AND of two input bits, implies non-interactive key exchange, and is therefore unlikely to be implied by public-key encryption or even oblivious transfer. - Applications. We present two types of applications of HSS. First, we construct 2-round protocols for secure multiparty computation from a simple constant-size instance of HSS. As a corollary, we obtain 2-round protocols with attractive asymptotic efficiency features under the Decision Diffie Hellman (DDH) assumption. Second, we use HSS to obtain nearly optimal worst-case to average-case reductions in P. This in turn has applications to fine-grained average-case hardness and verifiable computation

Privacy-aware Security Applications in the Era of Internet of Things

In this dissertation, we introduce several novel privacy-aware security applications. We split these contributions into three main categories: First, to strengthen the current authentication mechanisms, we designed two novel privacy-aware alternative complementary authentication mechanisms, Continuous Authentication (CA) and Multi-factor Authentication (MFA). Our first system is Wearable-assisted Continuous Authentication (WACA), where we used the sensor data collected from a wrist-worn device to authenticate users continuously. Then, we improved WACA by integrating a noise-tolerant template matching technique called NTT-Sec to make it privacy-aware as the collected data can be sensitive. We also designed a novel, lightweight, Privacy-aware Continuous Authentication (PACA) protocol. PACA is easily applicable to other biometric authentication mechanisms when feature vectors are represented as fixed-length real-valued vectors. In addition to CA, we also introduced a privacy-aware multi-factor authentication method, called PINTA. In PINTA, we used fuzzy hashing and homomorphic encryption mechanisms to protect the users\u27 sensitive profiles while providing privacy-preserving authentication. For the second privacy-aware contribution, we designed a multi-stage privacy attack to smart home users using the wireless network traffic generated during the communication of the devices. The attack works even on the encrypted data as it is only using the metadata of the network traffic. Moreover, we also designed a novel solution based on the generation of spoofed traffic. Finally, we introduced two privacy-aware secure data exchange mechanisms, which allow sharing the data between multiple parties (e.g., companies, hospitals) while preserving the privacy of the individual in the dataset. These mechanisms were realized with the combination of Secure Multiparty Computation (SMC) and Differential Privacy (DP) techniques. In addition, we designed a policy language, called Curie Policy Language (CPL), to handle the conflicting relationships among parties. The novel methods, attacks, and countermeasures in this dissertation were verified with theoretical analysis and extensive experiments with real devices and users. We believe that the research in this dissertation has far-reaching implications on privacy-aware alternative complementary authentication methods, smart home user privacy research, as well as the privacy-aware and secure data exchange methods

Asymmetric Multi-Party Computation

Current protocols for Multi-Party Computation (MPC) consider the setting where all parties have access to similar resources. For example, all parties have access to channels bounded by the same worst-case delay upper bound ?, and all channels have the same cost of communication. As a consequence, the overall protocol performance (resp. the communication cost) may be heavily affected by the slowest (resp. the most expensive) channel, even when most channels are fast (resp. cheap). Given the state of affairs, we initiate a systematic study of asymmetric MPC. In asymmetric MPC, the parties are divided into two categories: fast and slow parties, depending on whether they have access to high-end or low-end resources. We investigate two different models. In the first, we consider asymmetric communication delays: Fast parties are connected via channels with small delay ? among themselves, while channels connected to (at least) one slow party have a large delay ? ? ?. In the second model, we consider asymmetric communication costs: Fast parties benefit from channels with cheap communication, while channels connected to a slow party have an expensive communication. We provide a wide range of positive and negative results exploring the trade-offs between the achievable number of tolerated corruptions t and slow parties s, versus the round complexity and communication cost in each of the models. Among others, we achieve the following results. In the model with asymmetric communication delays, focusing on the information-theoretic (i-t) setting: - An i-t asymmetric MPC protocol with security with abort as long as t+s < n and t < n/2, in a constant number of slow rounds. - We show that achieving an i-t asymmetric MPC protocol for t+s = n and with number of slow rounds independent of the circuit size implies an i-t synchronous MPC protocol with round complexity independent of the circuit size, which is a major problem in the field of round-complexity of MPC. - We identify a new primitive, asymmetric broadcast, that allows to consistently distribute a value among the fast parties, and at a later time the same value to slow parties. We completely characterize the feasibility of asymmetric broadcast by showing that it is possible if and only if 2t + s < n. - An i-t asymmetric MPC protocol with guaranteed output delivery as long as t+s < n and t < n/2, in a number of slow rounds independent of the circuit size. In the model with asymmetric communication cost, we achieve an asymmetric MPC protocol for security with abort for t+s < n and t < n/2, based on one-way functions (OWF). The protocol communicates a number of bits over expensive channels that is independent of the circuit size. We conjecture that assuming OWF is needed and further provide a partial result in this direction

Collaborative Privacy-Preserving Analysis of Oncological Data using Multiparty Homomorphic Encryption

Real-world healthcare data sharing is instrumental in constructing broader-based and larger clinical data sets that may improve clinical decision-making research and outcomes. Stakeholders are frequently reluctant to share their data without guaranteed patient privacy, proper protection of their data sets, and control over the usage of their data. Fully homomorphic encryption (FHE) is a cryptographic capability that can address these issues by enabling computation on encrypted data without intermediate decryptions, so the analytics results are obtained without revealing the raw data. This work presents a toolset for collaborative privacy-preserving analysis of oncological data using multiparty FHE. Our toolset supports survival analysis, logistic regression training, and several common descriptive statistics. We demonstrate using oncological data sets that the toolset achieves high accuracy and practical performance, which scales well to larger data sets. As part of this work, we propose a novel cryptographic protocol for interactive bootstrapping in multiparty FHE, which is of independent interest. The toolset we develop is general-purpose and can be applied to other collaborative medical and healthcare application domains

Practical and Efficient FHE-based MPC

We present a reactive MPC protocol built from FHE which is robust in the presence of active adversaries. In addition the protocol enables reduced bandwidth via means of transciphering, and also enables more expressive/efficient programs via means of a $\mathsf{Declassify}$ operation. All sub-components of the protocol can be efficiently realised using existing technology. We prove our protocol secure in the UC framework