1,025 research outputs found
Scalable and Secure Aggregation in Distributed Networks
We consider the problem of computing an aggregation function in a
\emph{secure} and \emph{scalable} way. Whereas previous distributed solutions
with similar security guarantees have a communication cost of , we
present a distributed protocol that requires only a communication complexity of
, which we prove is near-optimal. Our protocol ensures perfect
security against a computationally-bounded adversary, tolerates
malicious nodes for any constant (not
depending on ), and outputs the exact value of the aggregated function with
high probability
Resource-Efficient and Robust Distributed Computing
There has been a tremendous growth in the size of distributed systems in the past three decades. Today, distributed systems, such as the Internet, have become so large that they require highly scalable algorithms; algorithms that have asymptotically-small communication, computation, and latency costs with respect to the network size. Moreover, systems with thousands or even millions of parties distributed throughout the world is likely in danger of faults from untrusted parties. In this dissertation, we study scalable and secure distributed algorithms that can tolerate faults from untrusted parties. Throughout this work, we balance two important and often conflicting characteristics of distributed protocols: security and efficiency. Our first result is a protocol that solves the MPC problem in polylogarithmic communication and computation cost and is secure against an adversary than can corrupt a third of the parties. We adapted our synchronous MPC protocol to the asynchronous setting when the fraction of the corrupted parties are less than 1/8. Next, we presented a scalable protocol that solves the secret sharing problem between rational parties in polylogarithmic communication and computation cost. Furthermore, we presented a protocol that can solve the interactive communication problem over a noisy channel when the noise rate in unknown. In this problem, we have focused on the cost of the protocol in the resource-competitive analysis model. Unlike classic models, resource-competitive models consider the cost that the adversary must pay to succeed in corrupting the protocol
A Hybrid Approach to Privacy-Preserving Federated Learning
Federated learning facilitates the collaborative training of models without
the sharing of raw data. However, recent attacks demonstrate that simply
maintaining data locality during training processes does not provide sufficient
privacy guarantees. Rather, we need a federated learning system capable of
preventing inference over both the messages exchanged during training and the
final trained model while ensuring the resulting model also has acceptable
predictive accuracy. Existing federated learning approaches either use secure
multiparty computation (SMC) which is vulnerable to inference or differential
privacy which can lead to low accuracy given a large number of parties with
relatively small amounts of data each. In this paper, we present an alternative
approach that utilizes both differential privacy and SMC to balance these
trade-offs. Combining differential privacy with secure multiparty computation
enables us to reduce the growth of noise injection as the number of parties
increases without sacrificing privacy while maintaining a pre-defined rate of
trust. Our system is therefore a scalable approach that protects against
inference threats and produces models with high accuracy. Additionally, our
system can be used to train a variety of machine learning models, which we
validate with experimental results on 3 different machine learning algorithms.
Our experiments demonstrate that our approach out-performs state of the art
solutions
Quantum Clock Synchronization with a Single Qudit
Clock synchronization for nonfaulty processes in multiprocess networks is
indispensable for a variety of technologies. A reliable system must be able to
resynchronize the nonfaulty processes upon some components failing causing the
distribution of incorrect or conflicting information in the network. The task
of synchronizing such networks is related to detectable Byzantine agreement
(DBA), which can classically be solved using recursive algorithms if and only
if less than one-third of the processes are faulty. Here we introduce a
nonrecursive quantum algorithm that solves the DBA and achieves clock
synchronization in the presence of arbitrary many faulty processes by using
only a single quantum system
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
SPAM: Secure & Private Aircraft Management
With the rising use of aircrafts for operations ranging from disaster-relief
to warfare, there is a growing risk of adversarial attacks. Malicious entities
often only require the location of the aircraft for these attacks. Current
satellite-aircraft communication and tracking protocols put aircrafts at risk
if the satellite is compromised, due to computation being done in plaintext. In
this work, we present \texttt{SPAM}, a private, secure, and accurate system
that allows satellites to efficiently manage and maintain tracking angles for
aircraft fleets without learning aircrafts' locations. \texttt{SPAM} is built
upon multi-party computation and zero-knowledge proofs to guarantee privacy and
high efficiency. While catered towards aircrafts, \texttt{SPAM}'s
zero-knowledge fleet management can be easily extended to the IoT, with very
little overhead.Comment: 6 page
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