Secure Computation in Heterogeneous Environments: How to Bring Multiparty Computation Closer to Practice?

Many services that people use daily require computation that depends on the private data of multiple parties. While the utility of the final result of such interactions outweighs the privacy concerns related to output release, the inputs for such computations are much more sensitive and need to be protected. Secure multiparty computation (MPC) considers the question of constructing computation protocols that reveal nothing more about their inputs than what is inherently leaked by the output. There have been strong theoretical results that demonstrate that every functionality can be computed securely. However, these protocols remain unused in practical solutions since they introduce efficiency overhead prohibitive for most applications. Generic multiparty computation techniques address homogeneous setups with respect to the resources available to the participants and the adversarial model. On the other hand, realistic scenarios present a wide diversity of heterogeneous environments where different participants have different available resources and different incentives to misbehave and collude. In this thesis we introduce techniques for multiparty computation that focus on heterogeneous settings. We present solutions tailored to address different types of asymmetric constraints and improve the efficiency of existing approaches in these scenarios. We tackle the question from three main directions: New Computational Models for MPC - We explore different computational models that enable us to overcome inherent inefficiencies of generic MPC solutions using circuit representation for the evaluated functionality. First, we show how we can use random access machines to construct MPC protocols that add only polylogarithmic overhead to the running time of the insecure version of the underlying functionality. This allows to achieve MPC constructions with computational complexity sublinear in the size for their inputs, which is very important for computations that use large databases. We also consider multivariate polynomials which yield more succinct representations for the functionalities they implement than circuits, and at the same time a large collection of problems are naturally and efficiently expressed as multivariate polynomials. We construct an MPC protocol for multivariate polynomials, which improves the communication complexity of corresponding circuit solutions, and provides currently the most efficient solution for multiparty set intersection in the fully malicious case. Outsourcing Computation - The goal in this setting is to utilize the resources of a single powerful service provider for the work that computationally weak clients need to perform on their data. We present a new paradigm for constructing verifiable computation (VC) schemes, which enables a computationally limited client to verify efficiently the result of a large computation. Our construction is based on attribute-based encryption and avoids expensive primitives such as fully homomorphic encryption andprobabilistically checkable proofs underlying existing VC schemes. Additionally our solution enjoys two new useful properties: public delegation and verification. We further introduce the model of server-aided computation where we utilize the computational power of an outsourcing party to assist the execution and improve the efficiency of MPC protocols. For this purpose we define a new adversarial model of non-collusion, which provides room for more efficient constructions that rely almost completely only on symmetric key operations, and at the same time captures realistic settings for adversarial behavior. In this model we propose protocols for generic secure computation that offload the work of most of the parties to the computation server. We also construct a specialized server-aided two party set intersection protocol that achieves better efficiencies for the two participants than existing solutions. Outsourcing in many cases concerns only data storage and while outsourcing the data of a single party is useful, providing a way for data sharing among different clients of the service is the more interesting and useful setup. However, this scenario brings new challenges for access control since the access control rules and data accesses become private data for the clients with respect to the service provide. We propose an approach that offers trade-offs between the privacy provided for the clients and the communication overhead incurred for each data access. Efficient Private Search in Practice - We consider the question of private search from a different perspective compared to traditional settings for MPC. We start with strict efficiency requirements motivated by speeds of available hardware and what is considered acceptable overhead from practical point of view. Then we adopt relaxed definitions of privacy, which still provide meaningful security guarantees while allowing us to meet the efficiency requirements. In this setting we design a security architecture and implement a system for data sharing based on encrypted search, which achieves only 30% overhead compared to non-secure solutions on realistic workloads

Optimal-Rate Non-Committing Encryption in a CRS Model

Non-committing encryption (NCE) implements secure channels under adaptive corruptions in situations when data erasures are not trustworthy. In this paper we are interested in the rate of NCE, i.e. in how many bits the sender and receiver need to send per plaintext bit. In initial constructions (e.g. Canetti, Feige, Goldreich and Naor, STOC 96) the length of both the receiver message, namely the public key, and the sender message, namely the ciphertext, is m * poly(k) for an m-bit message, where k is the security parameter. Subsequent works improve efficiency significantly, achieving rate polylog(k). We construct the first constant-rate NCE. In fact, our scheme has rate 1+o(1), which is comparable to the rate of plain semantically secure encryption. Our scheme operates in the common reference string (CRS) model. Our CRS has size poly(m, k), but it is reusable for an arbitrary polynomial number of m-bit messages. In addition, it is the first NCE protocol with perfect correctness. We assume one way functions and indistinguishability obfuscation for circuits. As an essential step in our construction, we develop a technique for dealing with adversaries that modify the inputs to the protocol adaptively depending on a public key or CRS that contains obfuscated programs, while assuming only standard (polynomial) hardness of the obfuscation mechanism. This technique may well be useful elsewhere

OpenTor: Anonymity as a Commodity Service

Despite the growth of the Internet and the increasing concern for privacy of online communications, current deployments of anonymization networks depends on a very small set of nodes that volunteer their bandwidth. We believe that the main reason is not disbelief in their ability to protect anonymity, but rather the practical limitations in bandwidth and latency that stem from limited participation. This limited participation, in turn, is due to a lack of incentives. We propose providing economic incentives, which historically have worked very well. In this technical report, we demonstrate a payment scheme that can be used to compensate nodes which provide anonymity in Tor, an existing onion routing, anonymizing network. We show that current anonymous payment schemes are not suitable and introduce a hybrid payment system based on a combination of the Peppercoin Micropayment system and a new type of "one use" electronic cash. Our system claims to maintain users' anonymity, although payment techniques mentioned previously --- when adopted individually --- provably fail

RapidChain: Scaling Blockchain via Full Sharding

A major approach to overcoming the performance and scalability limitations of current blockchain protocols is to use sharding, which is to split the overheads of processing transactions among multiple, smaller groups of nodes. These groups work in parallel to maximize performance while requiring significantly smaller communication, computation, and storage per node, allowing the system to scale to large networks. However, existing sharding-based blockchain protocols still require a linear amount of communication (in the number of participants) per transaction, and hence, attain only partially the potential benefits of sharding. We show that this introduces a major bottleneck to the throughput and latency of these protocols. Aside from the limited scalability, these protocols achieve weak security guarantees due to either a small fault resiliency (e.g., 1/8 and 1/4) or high failure probability, or they rely on strong assumptions (e.g., trusted setup) that limit their applicability to mainstream payment systems. We propose RapidChain, the first sharding-based public blockchain protocol that is resilient to Byzantine faults from up to a 1/3 fraction of its participants, and achieves complete sharding of the communication, computation, and storage overhead of processing transactions without assuming any trusted setup. We introduce an optimal intra-committee consensus algorithm that can achieve very high throughputs via block pipelining, a novel gossiping protocol for large blocks, and a provably-secure reconfiguration mechanism to ensure robustness. Using an efficient cross-shard transaction verification technique, RapidChain avoids gossiping transactions to the entire network. Our empirical evaluations suggest that RapidChain can process (and confirm) more than 7,300 tx/sec with an expected confirmation latency of roughly 8.7 seconds in a network of 4,000 nodes with an overwhelming time-to-failure of more than 4,500 years

Anonymous Counting Tokens

We introduce a new primitive called anonymous counting tokens (ACTs) which allows clients to obtain blind signatures or MACs (aka tokens) on messages of their choice, while at the same time enabling issuers to enforce rate limits on the number of tokens that a client can obtain for each message. Our constructions enforce that each client will be able to obtain only one token per message and we show a generic transformation to support other rate limiting as well. We achieve this new property while maintaining the unforgeability and unlinkability properties required for anonymous tokens schemes. We present four ACT constructions with various trade-offs for their efficiency and underlying security assumptions. One construction uses factorization-based primitives and a cyclic group. It is secure in the random oracle model under the q-DDHI assumption (in a cyclic group) and the DCR assumption. Our three other constructions use bilinear maps: one is secure in the standard model under q-DDHI and SXDH, one is secure in the random oracle model under SXDH, and the most efficient of the three is secure in the random oracle model and generic bilinear group model

Outsourcing Multi-Party Computation

We initiate the study of secure multi-party computation (MPC) in a server-aided setting, where the parties have access to a single server that (1) does not have any input to the computation; (2) does not receive any output from the computation; but (3) has a vast (but bounded) amount of computational resources. In this setting, we are concerned with designing protocols that minimize the computation of the parties at the expense of the server. We develop new definitions of security for this server-aided setting, that generalize the standard simulation-based definitions for MPC, and allow us to formally capture the existence of dishonest but non-colluding participants. This requires us to introduce a formal characterization of non-colluding adversaries that may be of independent interest. We then design general and special-purpose server-aided MPC protocols that are more efficient (in terms of computation and communication) for the parties than the alternative of running a standard MPC protocol (i.e., without the server). Our main general-purpose protocol provides security when there is at least one honest party with input. We also construct a new and efficient server-aided protocol for private set intersection and give a general transformation from any secure delegated computation scheme to a server-aided two-party protocol

5Gen-C: Multi-input Functional Encryption and Program Obfuscation for Arithmetic Circuits

Program obfuscation is a powerful security primitive with many applications. White-box cryptography studies a particular subset of program obfuscation targeting keyed pseudorandom functions (PRFs), a core component of systems such as mobile payment and digital rights management. Although the white-box obfuscators currently used in practice do not come with security proofs and are thus routinely broken, recent years have seen an explosion of \emph{cryptographic} techniques for obfuscation, with the goal of avoiding this build-and-break cycle. In this work, we explore in detail cryptographic program obfuscation and the related primitive of multi-input functional encryption (MIFE). In particular, we extend the 5Gen framework (CCS 2016) to support circuit-based MIFE and program obfuscation, implementing both existing and new constructions. We then evaluate and compare the efficiency of these constructions in the context of PRF obfuscation. As part of this work we (1) introduce a novel instantiation of MIFE that works directly on functions represented as arithmetic circuits, (2) use a known transformation from MIFE to obfuscation to give us an obfuscator that performs better than all prior constructions, and (3) develop a compiler for generating circuits optimized for our schemes. Finally, we provide detailed experiments, demonstrating, among other things, the ability to obfuscate a PRF with a 64-bit key and 12 bits of input (containing 62k gates) in under 4 hours, with evaluation taking around 1 hour. This is by far the most complex function obfuscated to date

Make Some ROOM for the Zeros: Data Sparsity in Secure Distributed Machine Learning

Exploiting data sparsity is crucial for the scalability of many data analysis tasks. However, while there is an increasing interest in efficient secure computation protocols for distributed machine learning, data sparsity has so far not been considered in a principled way in that setting. We propose sparse data structures together with their corresponding secure computation protocols to address common data analysis tasks while utilizing data sparsity. In particular, we define a Read-Only Oblivious Map primitive (ROOM) for accessing elements in sparse structures, and present several instantiations of this primitive with different trade-offs. Then, using ROOM as a building block, we propose protocols for basic linear algebra operations such as Gather, Scatter, and multiple variants of sparse matrix multiplication. Our protocols are easily composable by using secret sharing. We leverage this, at the highest level of abstraction, to build secure end-to-end protocols for non-parametric models ($k$-nearest neighbors and naive Bayes classification) and parametric models (logistic regression) that enable secure analysis on high-dimensional datasets. The experimental evaluation of our protocol implementations demonstrates a manyfold improvement in the efficiency over state-of-the-art techniques across all applications. Our system is designed and built mirroring the modular architecture in scientific computing and machine learning frameworks, and inspired by the Sparse BLAS standard

Trade-offs in Private Search

Encrypted search -- performing queries on protected data -- is a well researched problem. However, existing solutions have inherent inefficiency that raises questions of practicality. Here, we step back from the goal of achieving maximal privacy guarantees in an encrypted search scenario to consider efficiency as a priority. We propose a privacy framework for search that allows tuning and optimization of the trade-offs between privacy and efficiency. As an instantiation of the privacy framework we introduce a tunable search system based on the SADS scheme and provide detailed measurements demonstrating the trade-offs of the constructed system. We also analyze other existing encrypted search schemes with respect to this framework. We further propose a protocol that addresses the challenge of document content retrieval in a search setting with relaxed privacy requirements