3,610 research outputs found
A Note on "Confidentiality-Preserving Image Search: A Comparative Study Between Homomorphic Encryption and Distance-Preserving Randomization"
Recently, Lu et al. have proposed two image search schemes based on additive
homomorphic encryption [IEEE Access, 2 (2014), 125-141]. We remark that both
two schemes are flawed because: (1) the first scheme does not make use of the
additive homomorphic property at all; (2) the additive homomorphic encryption
in the second scheme is unnecessary and can be replaced by a more efficient
symmetric key encryption
Fully Homomorphic Image Processing
Fully homomorphic encryption has allowed devices to outsource computation to
third parties while preserving the secrecy of the data being computed on. Many
images contain sensitive information and are commonly sent to cloud services to
encode images for different devices. We implement image processing
homomorphically that ensures secrecy of the image while also providing
reasonable overhead. We first present some previous related work, as well as
the fully homomorphic encryption scheme we use. Then, we introduce our schemes
for JPEG encoding and decoding, as well as schemes for bilinear and bicubic
image resizing, as well as some data and analysis of our homomorphic schemes.
Finally, we outline several issues with the homomorphic evaluation of
proprietary algorithms, and how a third party can gain information on the
algorithm through noise.Comment: 12 Page
Attacks on Image Encryption Schemes for Privacy-Preserving Deep Neural Networks
Privacy preserving machine learning is an active area of research usually
relying on techniques such as homomorphic encryption or secure multiparty
computation. Recent novel encryption techniques for performing machine learning
using deep neural nets on images have recently been proposed by Tanaka and
Sirichotedumrong, Kinoshita, and Kiya. We present new chosen-plaintext and
ciphertext-only attacks against both of these proposed image encryption schemes
and demonstrate the attacks' effectiveness on several examples.Comment: For associated code, see
https://github.com/ahchang98/image-encryption-scheme-attack
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption
We propose a privacy-preserving framework for learning visual classifiers by
leveraging distributed private image data. This framework is designed to
aggregate multiple classifiers updated locally using private data and to ensure
that no private information about the data is exposed during and after its
learning procedure. We utilize a homomorphic cryptosystem that can aggregate
the local classifiers while they are encrypted and thus kept secret. To
overcome the high computational cost of homomorphic encryption of
high-dimensional classifiers, we (1) impose sparsity constraints on local
classifier updates and (2) propose a novel efficient encryption scheme named
doubly-permuted homomorphic encryption (DPHE) which is tailored to sparse
high-dimensional data. DPHE (i) decomposes sparse data into its constituent
non-zero values and their corresponding support indices, (ii) applies
homomorphic encryption only to the non-zero values, and (iii) employs double
permutations on the support indices to make them secret. Our experimental
evaluation on several public datasets shows that the proposed approach achieves
comparable performance against state-of-the-art visual recognition methods
while preserving privacy and significantly outperforms other privacy-preserving
methods.Comment: To appear in ICCV 201
Fully Homomorphic Encryption Encapsulated Difference Expansion for Reversible Data hiding in Encrypted Domain
This paper proposes a fully homomorphic encryption encapsulated difference
expansion (FHEE-DE) scheme for reversible data hiding in encrypted domain
(RDH-ED). In the proposed scheme, we use key-switching and bootstrapping
techniques to control the ciphertext extension and decryption failure. To
realize the data extraction directly from the encrypted domain without the
private key, a key-switching based least-significant-bit (KS-LSB) data hiding
method has been designed. In application, the user first encrypts the plaintext
and uploads ciphertext to the server. Then the server performs data hiding by
FHEE-DE and KS-LSB to obtain the marked ciphertext. Additional data can be
extracted directly from the marked ciphertext by the server without the private
key. The user can decrypt the marked ciphertext to obtain the marked plaintext.
Then additional data or plaintext can be obtained from the marked plaintext by
using the standard DE extraction or recovery. A fidelity constraint of DE is
introduced to reduce the distortion of the marked plaintext. FHEE-DE enables
the server to implement FHEE-DE recovery or extraction on the marked
ciphertext, which returns the ciphertext of original plaintext or additional
data to the user. In addition, we simplified the homomorphic operations of the
proposed universal FHEE-DE to obtain an efficient version. The Experimental
results demonstrate that the embedding capacity, fidelity, and reversibility of
the proposed scheme are superior to existing RDH-ED methods, and fully
separability is achieved without reducing the security of encryption
Application of Lowner-John Ellipsoid in the Steganography of Lattice Vectors and a Review of The Gentry's FHE
In this paper, first, we utilize the Lowner-John ellipsoid of a convex set to
hide the lattice data information. We also describe the algorithm of
information recovery in polynomial time by employing the Todd-Khachyian
algorithm. The importance of lattice data is generally due to their
applications in the homomorphic encryption schemes. For this reason we also
outline the general scheme of a homomorphic encryption provided by Gentry
Multivariate Cryptosystems for Secure Processing of Multidimensional Signals
Multidimensional signals like 2-D and 3-D images or videos are inherently
sensitive signals which require privacy-preserving solutions when processed in
untrustworthy environments, but their efficient encrypted processing is
particularly challenging due to their structure, dimensionality and size. This
work introduces a new cryptographic hard problem denoted m-RLWE (multivariate
Ring Learning with Errors) which generalizes RLWE, and proposes several
relinearization-based techniques to efficiently convert signals with different
structures and dimensionalities. The proposed hard problem and the developed
techniques give support to lattice cryptosystems that enable encrypted
processing of multidimensional signals and efficient conversion between
different structures. We show an example cryptosystem and prove that it
outperforms its RLWE counterpart in terms of security against basis-reduction
attacks, efficiency and cipher expansion for encrypted image processing, and we
exemplify some of the proposed transformation techniques in critical and
ubiquitous block-based processing application
Secure SURF with Fully Homomorphic Encryption
Cloud computing is an important part of today's world because offloading
computations is a method to reduce costs. In this paper, we investigate
computing the Speeded Up Robust Features (SURF) using Fully Homomorphic
Encryption (FHE). Performing SURF in FHE enables a method to offload the
computations while maintaining security and privacy of the original data. In
support of this research, we developed a framework to compute SURF via a
rational number based compatible with FHE. Although floating point (R) to
rational numbers (Q) conversion introduces error, our research provides tight
bounds on the magnitude of error in terms of parameters of FHE. We empirically
verified the proposed method against a set of images at different sizes and
showed that our framework accurately computes most of the SURF keypoints in
FHE
SEALion: a Framework for Neural Network Inference on Encrypted Data
We present SEALion: an extensible framework for privacy-preserving machine
learning with homomorphic encryption. It allows one to learn deep neural
networks that can be seamlessly utilized for prediction on encrypted data. The
framework consists of two layers: the first is built upon TensorFlow and SEAL
and exposes standard algebra and deep learning primitives; the second
implements a Keras-like syntax for training and inference with neural networks.
Given a required level of security, a user is abstracted from the details of
the encoding and the encryption scheme, allowing quick prototyping. We present
two applications that exemplifying the extensibility of our proposal, which are
also of independent interest: i) improving efficiency of neural network
inference by an activity sparsifier and ii) transfer learning by querying a
server-side Variational AutoEncoder that can handle encrypted data
Cloud-based Privacy Preserving Image Storage, Sharing and Search
High-resolution cameras produce huge volume of high quality images everyday.
It is extremely challenging to store, share and especially search those huge
images, for which increasing number of cloud services are presented to support
such functionalities. However, images tend to contain rich sensitive
information (\eg, people, location and event), and people's privacy concerns
hinder their readily participation into the services provided by untrusted
third parties. In this work, we introduce PIC: a Privacy-preserving large-scale
Image search system on Cloud. Our system enables efficient yet secure
content-based image search with fine-grained access control, and it also
provides privacy-preserving image storage and sharing among users. Users can
specify who can/cannot search on their images when using the system, and they
can search on others' images if they satisfy the condition specified by the
image owners. Majority of the computationally intensive jobs are outsourced to
the cloud side, and users only need to submit the query and receive the result
throughout the entire image search. Specially, to deal with massive images, we
design our system suitable for distributed and parallel computation and
introduce several optimizations to further expedite the search process. We
implement a prototype of PIC including both cloud side and client side. The
cloud side is a cluster of computers with distributed file system (Hadoop HDFS)
and MapReduce architecture (Hadoop MapReduce). The client side is built for
both Windows OS laptops and Android phones. We evaluate the prototype system
with large sets of real-life photos. Our security analysis and evaluation
results show that PIC successfully protect the image privacy at a low cost of
computation and communication.Comment: 15 pages, 12 figure
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