10,270 research outputs found
A Survey on Homomorphic Encryption Schemes: Theory and Implementation
Legacy encryption systems depend on sharing a key (public or private) among
the peers involved in exchanging an encrypted message. However, this approach
poses privacy concerns. Especially with popular cloud services, the control
over the privacy of the sensitive data is lost. Even when the keys are not
shared, the encrypted material is shared with a third party that does not
necessarily need to access the content. Moreover, untrusted servers, providers,
and cloud operators can keep identifying elements of users long after users end
the relationship with the services. Indeed, Homomorphic Encryption (HE), a
special kind of encryption scheme, can address these concerns as it allows any
third party to operate on the encrypted data without decrypting it in advance.
Although this extremely useful feature of the HE scheme has been known for over
30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE)
scheme, which allows any computable function to perform on the encrypted data,
was introduced by Craig Gentry in 2009. Even though this was a major
achievement, different implementations so far demonstrated that FHE still needs
to be improved significantly to be practical on every platform. First, we
present the basics of HE and the details of the well-known Partially
Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which
are important pillars of achieving FHE. Then, the main FHE families, which have
become the base for the other follow-up FHE schemes are presented. Furthermore,
the implementations and recent improvements in Gentry-type FHE schemes are also
surveyed. Finally, further research directions are discussed. This survey is
intended to give a clear knowledge and foundation to researchers and
practitioners interested in knowing, applying, as well as extending the state
of the art HE, PHE, SWHE, and FHE systems.Comment: - Updated. (October 6, 2017) - This paper is an early draft of the
survey that is being submitted to ACM CSUR and has been uploaded to arXiv for
feedback from stakeholder
Effective fault-tolerant quantum computation with slow measurements
How important is fast measurement for fault-tolerant quantum computation?
Using a combination of existing and new ideas, we argue that measurement times
as long as even 1,000 gate times or more have a very minimal effect on the
quantum accuracy threshold. This shows that slow measurement, which appears to
be unavoidable in many implementations of quantum computing, poses no essential
obstacle to scalability.Comment: 9 pages, 11 figures. v2: small changes and reference addition
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Near-sensor data analytics is a promising direction for IoT endpoints, as it
minimizes energy spent on communication and reduces network load - but it also
poses security concerns, as valuable data is stored or sent over the network at
various stages of the analytics pipeline. Using encryption to protect sensitive
data at the boundary of the on-chip analytics engine is a way to address data
security issues. To cope with the combined workload of analytics and encryption
in a tight power envelope, we propose Fulmine, a System-on-Chip based on a
tightly-coupled multi-core cluster augmented with specialized blocks for
compute-intensive data processing and encryption functions, supporting software
programmability for regular computing tasks. The Fulmine SoC, fabricated in
65nm technology, consumes less than 20mW on average at 0.8V achieving an
efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to
25MIPS/mW in software. As a strong argument for real-life flexible application
of our platform, we show experimental results for three secure analytics use
cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN
consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with
secured remote recognition in 5.74pJ/op; and seizure detection with encrypted
data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE
Transactions on Circuits and Systems - I: Regular Paper
Secure -ish Nearest Neighbors Classifier
In machine learning, classifiers are used to predict a class of a given query
based on an existing (classified) database. Given a database S of n
d-dimensional points and a d-dimensional query q, the k-nearest neighbors (kNN)
classifier assigns q with the majority class of its k nearest neighbors in S.
In the secure version of kNN, S and q are owned by two different parties that
do not want to share their data. Unfortunately, all known solutions for secure
kNN either require a large communication complexity between the parties, or are
very inefficient to run.
In this work we present a classifier based on kNN, that can be implemented
efficiently with homomorphic encryption (HE). The efficiency of our classifier
comes from a relaxation we make on kNN, where we allow it to consider kappa
nearest neighbors for kappa ~ k with some probability. We therefore call our
classifier k-ish Nearest Neighbors (k-ish NN).
The success probability of our solution depends on the distribution of the
distances from q to S and increase as its statistical distance to Gaussian
decrease.
To implement our classifier we introduce the concept of double-blinded
coin-toss. In a doubly-blinded coin-toss the success probability as well as the
output of the toss are encrypted. We use this coin-toss to efficiently
approximate the average and variance of the distances from q to S. We believe
these two techniques may be of independent interest.
When implemented with HE, the k-ish NN has a circuit depth that is
independent of n, therefore making it scalable. We also implemented our
classifier in an open source library based on HELib and tested it on a breast
tumor database. The accuracy of our classifier (F_1 score) were 98\% and
classification took less than 3 hours compared to (estimated) weeks in current
HE implementations
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