158 research outputs found
Privacy-Preserving Data Falsification Detection in Smart Grids using Elliptic Curve Cryptography and Homomorphic Encryption
In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers\u27 fine-grained power consumption data; however, analyzing customers\u27 private data violates the customers\u27 privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme
Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption
The Machine Learning and Deep Learning Models require a lot of data for the
training process, and in some scenarios, there might be some sensitive data,
such as customer information involved, which the organizations might be
hesitant to outsource for model building. Some of the privacy-preserving
techniques such as Differential Privacy, Homomorphic Encryption, and Secure
Multi-Party Computation can be integrated with different Machine Learning and
Deep Learning algorithms to provide security to the data as well as the model.
In this paper, we propose a Chaotic Extreme Learning Machine and its encrypted
form using Fully Homomorphic Encryption where the weights and biases are
generated using a logistic map instead of uniform distribution. Our proposed
method has performed either better or similar to the Traditional Extreme
Learning Machine on most of the datasets.Comment: 26 pages; 1 Figure; 7 Tables. arXiv admin note: text overlap with
arXiv:2205.1326
Malware-Resistant Protocols for Real-World Systems
Cryptographic protocols are widely used to protect real-world systems from attacks. Paying for goods in a shop, withdrawing money or browsing the Web; all these activities are backed by cryptographic protocols. However, in recent years a potent threat became apparent. Malware is increasingly used in attacks to bypass existing security mechanisms. Many cryptographic protocols that are used in real-world systems today have been found to be susceptible to malware attacks. One reason for this is that most of these protocols were designed with respect to the Dolev-Yao attack model that assumes an attacker to control the network between computer systems but not the systems themselves. Furthermore, most real-world protocols do not provide a formal proof of security and thus lack a precise definition of the security goals the designers tried to achieve. This work tackles the design of cryptographic protocols that are resilient to malware attacks, applicable to real-world systems, and provably secure.
In this regard, we investigate three real-world use cases: electronic payment, web authentication, and data aggregation. We analyze the security of existing protocols and confirm results from prior work that most protocols are not resilient to malware. Furthermore, we provide guidelines for the design of malware-resistant protocols and propose such protocols. In addition, we formalize security notions for malware-resistance and use a formal proof of security to verify the security guarantees of our protocols.
In this work we show that designing malware-resistant protocols for real-world systems is possible. We present a new security notion for electronic payment and web authentication, called one-out-of-two security, that does not require a single device to be trusted and ensures that a protocol stays secure as long as one of two devices is not compromised. Furthermore, we propose L-Pay, a cryptographic protocol for paying at the point of sale (POS) or withdrawing money at an automated teller machine (ATM) satisfying one-out-of-two security, FIDO2 With Two Displays (FIDO2D) a cryptographic protocol to secure transactions in the Web with one-out-of-two security and Secure Aggregation Grouped by Multiple Attributes (SAGMA), a cryptographic protocol for secure data aggregation in encrypted databases.
In this work, we take important steps towards the use of malware-resistant protocols in real-world systems. Our guidelines and protocols can serve as templates to design new cryptographic protocols and improve security in further use cases
A Privacy-Preserving Outsourced Data Model in Cloud Environment
Nowadays, more and more machine learning applications, such as medical
diagnosis, online fraud detection, email spam filtering, etc., services are
provided by cloud computing. The cloud service provider collects the data from
the various owners to train or classify the machine learning system in the
cloud environment. However, multiple data owners may not entirely rely on the
cloud platform that a third party engages. Therefore, data security and privacy
problems are among the critical hindrances to using machine learning tools,
particularly with multiple data owners. In addition, unauthorized entities can
detect the statistical input data and infer the machine learning model
parameters. Therefore, a privacy-preserving model is proposed, which protects
the privacy of the data without compromising machine learning efficiency. In
order to protect the data of data owners, the epsilon-differential privacy is
used, and fog nodes are used to address the problem of the lower bandwidth and
latency in this proposed scheme. The noise is produced by the
epsilon-differential mechanism, which is then added to the data. Moreover, the
noise is injected at the data owner site to protect the owners data. Fog nodes
collect the noise-added data from the data owners, then shift it to the cloud
platform for storage, computation, and performing the classification tasks
purposes
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
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Federated learning is a distributed framework for training machine learning
models over the data residing at mobile devices, while protecting the privacy
of individual users. A major bottleneck in scaling federated learning to a
large number of users is the overhead of secure model aggregation across many
users. In particular, the overhead of the state-of-the-art protocols for secure
model aggregation grows quadratically with the number of users. In this paper,
we propose the first secure aggregation framework, named Turbo-Aggregate, that
in a network with users achieves a secure aggregation overhead of
, as opposed to , while tolerating up to a user dropout
rate of . Turbo-Aggregate employs a multi-group circular strategy for
efficient model aggregation, and leverages additive secret sharing and novel
coding techniques for injecting aggregation redundancy in order to handle user
dropouts while guaranteeing user privacy. We experimentally demonstrate that
Turbo-Aggregate achieves a total running time that grows almost linear in the
number of users, and provides up to speedup over the
state-of-the-art protocols with up to users. Our experiments also
demonstrate the impact of model size and bandwidth on the performance of
Turbo-Aggregate
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Big data is one of the cornerstones to enabling and training deep neural
networks (DNNs). Because of the lack of expertise, to gain benefits from their
data, average users have to rely on and upload their private data to big data
companies they may not trust. Due to the compliance, legal, or privacy
constraints, most users are willing to contribute only their encrypted data,
and lack interests or resources to join the training of DNNs in cloud. To train
a DNN on encrypted data in a completely non-interactive way, a recent work
proposes a fully homomorphic encryption (FHE)-based technique implementing all
activations in the neural network by \textit{Brakerski-Gentry-Vaikuntanathan
(BGV)}-based lookup tables. However, such inefficient lookup-table-based
activations significantly prolong the training latency of privacy-preserving
DNNs.
In this paper, we propose, Glyph, a FHE-based scheme to fast and accurately
train DNNs on encrypted data by switching between TFHE (Fast Fully Homomorphic
Encryption over the Torus) and BGV cryptosystems. Glyph uses
logic-operation-friendly TFHE to implement nonlinear activations, while adopts
vectorial-arithmetic-friendly BGV to perform multiply-accumulation (MAC)
operations. Glyph further applies transfer learning on the training of DNNs to
improve the test accuracy and reduce the number of MAC operations between
ciphertext and ciphertext in convolutional layers. Our experimental results
show Glyph obtains the state-of-the-art test accuracy, but reduces the training
latency by over the prior FHE-based technique on various encrypted
datasets.Comment: 10 pages, 8 figure
- …