556 research outputs found
Efficient Oblivious Branching Programs for Threshold and Mod Functions
AbstractIn his survey paper on branching programs, Razborov asked the following question: Does every rectifier-switching network computing the majority ofnbits have sizen1+Ω(1)? We answer this question in the negative by constructing a simple oblivious branching program of sizeO[nlog3n/loglognlogloglogn] for computing any threshold function. This improves the previously best known upper bound ofO(n3/2) due to Lupanov. We also construct oblivious branching programs of sizeo(nlog4n) for computing general mod functions. All previously known constructions for computing general mod functions have sizeΩ(n3/2)
Privacy-Aware Processing of Biometric Templates by Means of Secure Two-Party Computation
The use of biometric data for person identification and access control is gaining more and more popularity. Handling biometric data, however, requires particular care, since biometric data is indissolubly tied to the identity of the owner hence raising important security and privacy issues. This chapter focuses on the latter, presenting an innovative approach that, by relying on tools borrowed from Secure Two Party Computation (STPC) theory, permits to process the biometric data in encrypted form, thus eliminating any risk that private biometric information is leaked during an identification process. The basic concepts behind STPC are reviewed together with the basic cryptographic primitives needed to achieve privacy-aware processing of biometric data in a STPC context. The two main approaches proposed so far, namely homomorphic encryption and garbled circuits, are discussed and the way such techniques can be used to develop a full biometric matching protocol described. Some general guidelines to be used in the design of a privacy-aware biometric system are given, so as to allow the reader to choose the most appropriate tools depending on the application at hand
Complexity of Restricted and Unrestricted Models of Molecular Computation
In [9] and [2] a formal model for molecular computing was
proposed, which makes focused use of affinity purification.
The use of PCR was suggested to expand the range of
feasible computations, resulting in a second model. In this
note, we give a precise characterization of these two models
in terms of recognized computational complexity classes,
namely branching programs (BP) and nondeterministic
branching programs (NBP) respectively. This allows us to
give upper and lower bounds on the complexity of desired
computations. Examples are given of computable and
uncomputable problems, given limited time
Pseudorandomness and Fourier Growth Bounds for Width-3 Branching Programs
We present an explicit pseudorandom generator for oblivious, read-once, width-3 branching programs, which can read their input bits in any order. The generator has seed length O~( log^3 n ).
The previously best known seed length for this model is n^{1/2+o(1)} due to Impagliazzo, Meka, and Zuckerman (FOCS\u2712). Our work generalizes a recent result of Reingold, Steinke, and Vadhan (RANDOM\u2713) for permutation branching programs. The main technical novelty underlying our generator is a new bound on the Fourier growth of width-3, oblivious, read-once branching programs. Specifically, we show that for any f : {0,1}^n -> {0,1} computed by such a branching program, and k in [n], sum_{|s|=k} |hat{f}(s)| < n^2 * (O(log n))^k,
where f(x) = sum_s hat{f}(s) (-1)^ is the standard Fourier transform over Z_2^n. The base O(log n) of the Fourier growth is tight up to a factor of log log n
XONN: XNOR-based Oblivious Deep Neural Network Inference
Advancements in deep learning enable cloud servers to provide
inference-as-a-service for clients. In this scenario, clients send their raw
data to the server to run the deep learning model and send back the results.
One standing challenge in this setting is to ensure the privacy of the clients'
sensitive data. Oblivious inference is the task of running the neural network
on the client's input without disclosing the input or the result to the server.
This paper introduces XONN, a novel end-to-end framework based on Yao's Garbled
Circuits (GC) protocol, that provides a paradigm shift in the conceptual and
practical realization of oblivious inference. In XONN, the costly
matrix-multiplication operations of the deep learning model are replaced with
XNOR operations that are essentially free in GC. We further provide a novel
algorithm that customizes the neural network such that the runtime of the GC
protocol is minimized without sacrificing the inference accuracy.
We design a user-friendly high-level API for XONN, allowing expression of the
deep learning model architecture in an unprecedented level of abstraction.
Extensive proof-of-concept evaluation on various neural network architectures
demonstrates that XONN outperforms prior art such as Gazelle (USENIX
Security'18) by up to 7x, MiniONN (ACM CCS'17) by 93x, and SecureML (IEEE
S&P'17) by 37x. State-of-the-art frameworks require one round of interaction
between the client and the server for each layer of the neural network,
whereas, XONN requires a constant round of interactions for any number of
layers in the model. XONN is first to perform oblivious inference on Fitnet
architectures with up to 21 layers, suggesting a new level of scalability
compared with state-of-the-art. Moreover, we evaluate XONN on four datasets to
perform privacy-preserving medical diagnosis.Comment: To appear in USENIX Security 201
New Bounds for the Garden-Hose Model
We show new results about the garden-hose model. Our main results include
improved lower bounds based on non-deterministic communication complexity
(leading to the previously unknown bounds for Inner Product mod 2
and Disjointness), as well as an upper bound for the
Distributed Majority function (previously conjectured to have quadratic
complexity). We show an efficient simulation of formulae made of AND, OR, XOR
gates in the garden-hose model, which implies that lower bounds on the
garden-hose complexity of the order will be
hard to obtain for explicit functions. Furthermore we study a time-bounded
variant of the model, in which even modest savings in time can lead to
exponential lower bounds on the size of garden-hose protocols.Comment: In FSTTCS 201
- …