29 research outputs found
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare
To train sophisticated machine learning models one usually needs many
training samples. Especially in healthcare settings these samples can be very
expensive, meaning that one institution alone usually does not have enough on
its own. Merging privacy-sensitive data from different sources is usually
restricted by data security and data protection measures. This can lead to
approaches that reduce data quality by putting noise onto the variables (e.g.,
in -differential privacy) or omitting certain values (e.g., for
-anonymity). Other measures based on cryptographic methods can lead to very
time-consuming computations, which is especially problematic for larger
multi-omics data. We address this problem by introducing ESCAPED, which stands
for Efficient SeCure And PrivatE Dot product framework, enabling the
computation of the dot product of vectors from multiple sources on a
third-party, which later trains kernel-based machine learning algorithms, while
neither sacrificing privacy nor adding noise. We evaluated our framework on
drug resistance prediction for HIV-infected people and multi-omics
dimensionality reduction and clustering problems in precision medicine. In
terms of execution time, our framework significantly outperforms the
best-fitting existing approaches without sacrificing the performance of the
algorithm. Even though we only show the benefit for kernel-based algorithms,
our framework can open up new research opportunities for further machine
learning models that require the dot product of vectors from multiple sources.Comment: AAAI 2021, Preprint version of the full paper with supplementary
materia
Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach
Several domains increasingly rely on machine learning in their applications.
The resulting heavy dependence on data has led to the emergence of various laws
and regulations around data ethics and privacy and growing awareness of the
need for privacy-preserving machine learning (ppML). Current ppML techniques
utilize methods that are either purely based on cryptography, such as
homomorphic encryption, or that introduce noise into the input, such as
differential privacy. The main criticism given to those techniques is the fact
that they either are too slow or they trade off a model s performance for
improved confidentiality. To address this performance reduction, we aim to
leverage robust representation learning as a way of encoding our data while
optimizing the privacy-utility trade-off. Our method centers on training
autoencoders in a multi-objective manner and then concatenating the latent and
learned features from the encoding part as the encoded form of our data. Such a
deep learning-powered encoding can then safely be sent to a third party for
intensive training and hyperparameter tuning. With our proposed framework, we
can share our data and use third party tools without being under the threat of
revealing its original form. We empirically validate our results on unimodal
and multimodal settings, the latter following a vertical splitting system and
show improved performance over state-of-the-art
Weaknesses in a Recently Proposed RFID Authentication Protocol
Abstract. Many RFID authentication protocols have been proposed to provide desired security and privacy level for RFID systems. Almost all of these protocols are based symmetric cryptography because of the limited resources of RFID tags. Recently Cheng et. al have been proposed an RFID security protocol based on chaotic maps. In this paper, we analyze the security of this protocol and discover its vulnerabilities. We firstly present a de-synchronization attack in which a passive adversary makes the shared secrets out-of-synchronization by eavesdropping just one protocol session. We secondly present a secret disclosure attack in which a passive adversary extracts secrets of a tag by eavesdropping just one protocol session. An adversary having the secrets of the tag can launch some other attacks