424 research outputs found
Efficient Scalable Constant-Round MPC via Garbled Circuits
In the setting of secure multiparty computation, a set of mutually distrustful parties carry out a joint computation of their inputs, without revealing anything but the output. Over recent years, there has been tremendous progress towards making secure computation practical, with great success in the two-party case. In contrast, in the multiparty case, progress has been much slower, even for the case of semi-honest adversaries.
In this paper, we consider the case of constant-round multiparty computation, via the garbled circuit approach of BMR (Beaver et al., STOC 1990). In recent work, it was shown that this protocol can be efficiently instantiated for semi-honest adversaries (Ben-Efraim et al., ACM CCS 2016). However, it scales very poorly with the number of parties, since the cost of garbled circuit evaluation is quadratic in the number of parties, per gate. Thus, for a large number of parties, it becomes expensive. We present a new way of constructing a BMR-type garbled circuit that can be evaluated with only a constant number of operations per gate. Our constructions use key-homomorphic pseudorandom functions (one based on DDH and the other on Ring-LWE) and are concretely efficient. In particular, for a large number of parties (e.g., 100), our new circuit can be evaluated faster than the standard BMR garbled circuit that uses only AES computations. Thus, our protocol is an important step towards achieving concretely efficient large-scale multiparty computation for Internet-like settings (where constant-round protocols are needed due to high latency)
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure
function evaluation (SFE) which enables two parties to jointly compute a
function without disclosing their private inputs. Chameleon combines the best
aspects of generic SFE protocols with the ones that are based upon additive
secret sharing. In particular, the framework performs linear operations in the
ring using additively secret shared values and nonlinear
operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson
protocol. Chameleon departs from the common assumption of additive or linear
secret sharing models where three or more parties need to communicate in the
online phase: the framework allows two parties with private inputs to
communicate in the online phase under the assumption of a third node generating
correlated randomness in an offline phase. Almost all of the heavy
cryptographic operations are precomputed in an offline phase which
substantially reduces the communication overhead. Chameleon is both scalable
and significantly more efficient than the ABY framework (NDSS'15) it is based
on. Our framework supports signed fixed-point numbers. In particular,
Chameleon's vector dot product of signed fixed-point numbers improves the
efficiency of mining and classification of encrypted data for algorithms based
upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer
convolutional deep neural network shows 133x and 4.2x faster executions than
Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively
Raziel: Private and Verifiable Smart Contracts on Blockchains
Raziel combines secure multi-party computation and proof-carrying code to
provide privacy, correctness and verifiability guarantees for smart contracts
on blockchains. Effectively solving DAO and Gyges attacks, this paper describes
an implementation and presents examples to demonstrate its practical viability
(e.g., private and verifiable crowdfundings and investment funds).
Additionally, we show how to use Zero-Knowledge Proofs of Proofs (i.e.,
Proof-Carrying Code certificates) to prove the validity of smart contracts to
third parties before their execution without revealing anything else. Finally,
we show how miners could get rewarded for generating pre-processing data for
secure multi-party computation.Comment: Support: cothority/ByzCoin/OmniLedge
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Cryptography
The Oberwolfach workshop Cryptography brought together scientists from cryptography with mathematicians specializing in the algorithmic problems underlying cryptographic security. The goal of the workshop was to stimulate interaction and collaboration that enables a holistic approach to designing cryptography from the mathematical foundations to practical applications. The workshop covered basic computational problems such as factoring and computing discrete logarithms and short vectors. It addressed fundamental research results leading to innovative cryptography for protecting security and privacy in cloud applications. It also covered some practical applications
Secure Computation Protocols for Privacy-Preserving Machine Learning
Machine Learning (ML) profitiert erheblich von der VerfĂŒgbarkeit groĂer Mengen an Trainingsdaten, sowohl im Bezug auf die Anzahl an Datenpunkten, als auch auf die Anzahl an Features pro Datenpunkt. Es ist allerdings oft weder möglich, noch gewollt, mehr Daten unter zentraler Kontrolle zu aggregieren. Multi-Party-Computation (MPC)-Protokolle stellen eine Lösung dieses Dilemmas in Aussicht, indem sie es mehreren Parteien erlauben, ML-Modelle auf der Gesamtheit ihrer Daten zu trainieren, ohne die Eingabedaten preiszugeben. Generische MPC-AnsĂ€tze bringen allerdings erheblichen Mehraufwand in der Kommunikations- und LaufzeitkomplexitĂ€t mit sich, wodurch sie sich nur beschrĂ€nkt fĂŒr den Einsatz in der Praxis eignen.
Das Ziel dieser Arbeit ist es, PrivatsphĂ€reerhaltendes Machine Learning mittels MPC praxistauglich zu machen. Zuerst fokussieren wir uns auf zwei Anwendungen, lineare Regression und Klassifikation von Dokumenten. Hier zeigen wir, dass sich der Kommunikations- und Rechenaufwand erheblich reduzieren lĂ€sst, indem die aufwĂ€ndigsten Teile der Berechnung durch Sub-Protokolle ersetzt werden, welche auf die Zusammensetzung der Parteien, die Verteilung der Daten, und die Zahlendarstellung zugeschnitten sind. Insbesondere das Ausnutzen dĂŒnnbesetzter DatenreprĂ€sentationen kann die Effizienz der Protokolle deutlich verbessern. Diese Beobachtung verallgemeinern wir anschlieĂend durch die Entwicklung einer Datenstruktur fĂŒr solch dĂŒnnbesetzte Daten, sowie dazugehöriger Zugriffsprotokolle. Aufbauend auf dieser Datenstruktur implementieren wir verschiedene Operationen der Linearen Algebra, welche in einer Vielzahl von Anwendungen genutzt werden.
Insgesamt zeigt die vorliegende Arbeit, dass MPC ein vielversprechendes Werkzeug auf dem Weg zu PrivatsphÀre-erhaltendem Machine Learning ist, und die von uns entwickelten Protokolle stellen einen wesentlichen Schritt in diese Richtung dar.Machine learning (ML) greatly benefits from the availability of large amounts of training data, both in terms of the number of samples, and the number of features per sample. However, aggregating more data under centralized control is not always possible, nor desirable, due to security and privacy concerns, regulation, or competition. Secure multi-party computation (MPC) protocols promise a solution to this dilemma, allowing multiple parties to train ML models on their joint datasets while provably preserving the confidentiality of the inputs. However, generic approaches to MPC result in large computation and communication overheads, which limits the applicability in practice.
The goal of this thesis is to make privacy-preserving machine learning with secure computation practical. First, we focus on two high-level applications, linear regression and document classification. We show that communication and computation overhead can be greatly reduced by identifying the costliest parts of the computation, and replacing them with sub-protocols that are tailored to the number and arrangement of parties, the data distribution, and the number representation used. One of our main findings is that exploiting sparsity in the data representation enables considerable efficiency improvements. We go on to generalize this observation, and implement a low-level data structure for sparse data, with corresponding secure access protocols. On top of this data structure, we develop several linear algebra algorithms that can be used in a wide range of applications. Finally, we turn to improving a cryptographic primitive named vector-OLE, for which we propose a novel protocol that helps speed up a wide range of secure computation tasks, within private machine learning and beyond.
Overall, our work shows that MPC indeed offers a promising avenue towards practical privacy-preserving machine learning, and the protocols we developed constitute a substantial step in that direction
MPC for MPC: Secure Computation on a Massively Parallel Computing Architecture
Massively Parallel Computation (MPC) is a model of computation widely believed to best capture realistic parallel computing architectures such as large-scale MapReduce and Hadoop clusters. Motivated by the fact that many data analytics tasks performed on these platforms involve sensitive user data, we initiate the theoretical exploration of how to leverage MPC architectures to enable efficient, privacy-preserving computation over massive data. Clearly if a computation task does not lend itself to an efficient implementation on MPC even without security, then we cannot hope to compute it efficiently on MPC with security. We show, on the other hand, that any task that can be efficiently computed on MPC can also be securely computed with comparable efficiency. Specifically, we show the following results:
- any MPC algorithm can be compiled to a communication-oblivious counterpart while asymptotically preserving its round and space complexity, where communication-obliviousness ensures that any network intermediary observing the communication patterns learn no information about the secret inputs;
- assuming the existence of Fully Homomorphic Encryption with a suitable notion of compactness and other standard cryptographic assumptions, any MPC algorithm can be compiled to a secure counterpart that defends against an adversary who controls not only intermediate network routers but additionally up to 1/3 - ? fraction of machines (for an arbitrarily small constant ?) - moreover, this compilation preserves the round complexity tightly, and preserves the space complexity upto a multiplicative security parameter related blowup.
As an initial exploration of this important direction, our work suggests new definitions and proposes novel protocols that blend algorithmic and cryptographic techniques
Towards Efficiency-Preserving Round Compression in MPC: Do fewer rounds mean more computation?
Reducing the rounds of interaction in secure multiparty computation (MPC) protocols has been the topic of study of many works. One popular approach to reduce rounds is to construct *round compression compilers*. A round compression compiler is one that takes a highly interactive protocol and transforms it into a protocol with far fewer rounds. The design of round compression compilers has traditionally focused on preserving the security properties of the underlying protocol and in particular, not much attention has been given towards preserving their computational and communication efficiency. Indeed, the recent round compression compilers that yield round-optimal MPC protocols incur large computational and communication overhead.
In this work, we initiate the study of *efficiency-preserving* round compression compilers, i.e. compilers that translate the efficiency benefits of the underlying highly interactive protocols to the fewer round setting. Focusing on the honest majority setting (with near-optimal corruption threshold , for any ), we devise a new compiler that yields two round (i.e., round optimal) semi-honest MPC with similar communication efficiency as the underlying (arbitrary round) protocol. By applying our compiler on the most efficient known MPC protocols, we obtain a two-round semi-honest protocol based on one-way functions, with total communication (and per-party computation) cost -- a significant improvement over prior two-round protocols with cost , where , is the size of the circuit computing the function and the corresponding depth. Our result can also be extended to handle malicious adversaries, either using stronger assumptions in the public key infrastructure (PKI) model, or in the plain model using an extra round.
An artifact of our approach is that the resultant protocol is ``unbalanced\u27\u27 in the amount of computation performed by different parties. We give evidence that this is *necessary* in our setting. Our impossibility result makes novel use of the ``MPC-in-the-head paradigm which has typically been used to demonstrate feasibility results
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation
Nowadays, gathering high-quality training data from multiple data controllers
with privacy preservation is a key challenge to train high-quality machine
learning models. The potential solutions could dramatically break the barriers
among isolated data corpus, and consequently enlarge the range of data
available for processing. To this end, both academia researchers and industrial
vendors are recently strongly motivated to propose two main-stream folders of
solutions: 1) Secure Multi-party Learning (MPL for short); and 2) Federated
Learning (FL for short). These two solutions have their advantages and
limitations when we evaluate them from privacy preservation, ways of
communication, communication overhead, format of data, the accuracy of trained
models, and application scenarios.
Motivated to demonstrate the research progress and discuss the insights on
the future directions, we thoroughly investigate these protocols and frameworks
of both MPL and FL. At first, we define the problem of training machine
learning models over multiple data sources with privacy-preserving (TMMPP for
short). Then, we compare the recent studies of TMMPP from the aspects of the
technical routes, parties supported, data partitioning, threat model, and
supported machine learning models, to show the advantages and limitations.
Next, we introduce the state-of-the-art platforms which support online training
over multiple data sources. Finally, we discuss the potential directions to
resolve the problem of TMMPP.Comment: 17 pages, 4 figure
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