5,967 research outputs found
Quantum computing on encrypted data
The ability to perform computations on encrypted data is a powerful tool for
protecting privacy. Recently, protocols to achieve this on classical computing
systems have been found. Here we present an efficient solution to the quantum
analogue of this problem that enables arbitrary quantum computations to be
carried out on encrypted quantum data. We prove that an untrusted server can
implement a universal set of quantum gates on encrypted quantum bits (qubits)
without learning any information about the inputs, while the client, knowing
the decryption key, can easily decrypt the results of the computation. We
experimentally demonstrate, using single photons and linear optics, the
encryption and decryption scheme on a set of gates sufficient for arbitrary
quantum computations. Because our protocol requires few extra resources
compared to other schemes it can be easily incorporated into the design of
future quantum servers. These results will play a key role in enabling the
development of secure distributed quantum systems
Enabling Privacy-preserving Auctions in Big Data
We study how to enable auctions in the big data context to solve many
upcoming data-based decision problems in the near future. We consider the
characteristics of the big data including, but not limited to, velocity,
volume, variety, and veracity, and we believe any auction mechanism design in
the future should take the following factors into consideration: 1) generality
(variety); 2) efficiency and scalability (velocity and volume); 3) truthfulness
and verifiability (veracity). In this paper, we propose a privacy-preserving
construction for auction mechanism design in the big data, which prevents
adversaries from learning unnecessary information except those implied in the
valid output of the auction. More specifically, we considered one of the most
general form of the auction (to deal with the variety), and greatly improved
the the efficiency and scalability by approximating the NP-hard problems and
avoiding the design based on garbled circuits (to deal with velocity and
volume), and finally prevented stakeholders from lying to each other for their
own benefit (to deal with the veracity). We achieve these by introducing a
novel privacy-preserving winner determination algorithm and a novel payment
mechanism. Additionally, we further employ a blind signature scheme as a
building block to let bidders verify the authenticity of their payment reported
by the auctioneer. The comparison with peer work shows that we improve the
asymptotic performance of peer works' overhead from the exponential growth to a
linear growth and from linear growth to a logarithmic growth, which greatly
improves the scalability
Measuring and mitigating AS-level adversaries against Tor
The popularity of Tor as an anonymity system has made it a popular target for
a variety of attacks. We focus on traffic correlation attacks, which are no
longer solely in the realm of academic research with recent revelations about
the NSA and GCHQ actively working to implement them in practice.
Our first contribution is an empirical study that allows us to gain a high
fidelity snapshot of the threat of traffic correlation attacks in the wild. We
find that up to 40% of all circuits created by Tor are vulnerable to attacks by
traffic correlation from Autonomous System (AS)-level adversaries, 42% from
colluding AS-level adversaries, and 85% from state-level adversaries. In
addition, we find that in some regions (notably, China and Iran) there exist
many cases where over 95% of all possible circuits are vulnerable to
correlation attacks, emphasizing the need for AS-aware relay-selection.
To mitigate the threat of such attacks, we build Astoria--an AS-aware Tor
client. Astoria leverages recent developments in network measurement to perform
path-prediction and intelligent relay selection. Astoria reduces the number of
vulnerable circuits to 2% against AS-level adversaries, under 5% against
colluding AS-level adversaries, and 25% against state-level adversaries. In
addition, Astoria load balances across the Tor network so as to not overload
any set of relays.Comment: Appearing at NDSS 201
Private Learning Implies Online Learning: An Efficient Reduction
We study the relationship between the notions of differentially private
learning and online learning in games. Several recent works have shown that
differentially private learning implies online learning, but an open problem of
Neel, Roth, and Wu \cite{NeelAaronRoth2018} asks whether this implication is
{\it efficient}. Specifically, does an efficient differentially private learner
imply an efficient online learner? In this paper we resolve this open question
in the context of pure differential privacy. We derive an efficient black-box
reduction from differentially private learning to online learning from expert
advice
Modeling and design of matching-critical circuits
Existing approaches for modeling mismatch effects in matching-critical circuits are based upon models derived under the widely accepted premise that distributed parameter devices can be modeled with lumped parameter models. It is shown in this dissertation that the lumped parameter models do not consistently reflect device performance and introduce substantial errors in matching-critical circuits if either systematic or random parameter variations occur in the channel. A new approach for characterizing the effects of both systematic and random variations in semiconductor device properties on device matching is introduced. This approach circumvents the introduction of model errors inherent in the existing approaches. A CAD tool, MOSGRAD, was developed to simulate the effects of distributed two-dimensional systematic and random variations in device parameters on the performance of matching-critical circuits. The tool is capable of predicting the performance of non-conventional circuit structures in which multiple drain and/or source regions that may or may not be rectangular and/or multiply segmented. Through the use of the tool, new current mirror layout strategies have been developed that exhibit reduced sensitivity to matching in the presence of linear parameter gradients
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