2,070 research outputs found
Image Classification using non-linear Support Vector Machines on Encrypted Data
In image processing, algorithms for object classification are typically based around machine learning. From the algorithm developer\u27s perspective, these can involve a considerable amount of effort and expertise to develop, which makes them commercially valuable. On the other hand, other parties may want to make use of these algorithms to classify their images, while protecting the privacy of their data. In this paper, we show how non-linear Support Vector Machines (SVMs) can be practically used for image classification on data encrypted with a Somewhat Homomorphic Encryption (SHE) scheme. Previous work has shown how an SVM with a linear kernel can be computed on encrypted data, but this only has limited applicability. By enabling SVMs with polynomial kernels, a much larger class of applications are possible with more accuracy in classification results
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure
function evaluation (SFE) which enables two parties to jointly compute a
function without disclosing their private inputs. Chameleon combines the best
aspects of generic SFE protocols with the ones that are based upon additive
secret sharing. In particular, the framework performs linear operations in the
ring using additively secret shared values and nonlinear
operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson
protocol. Chameleon departs from the common assumption of additive or linear
secret sharing models where three or more parties need to communicate in the
online phase: the framework allows two parties with private inputs to
communicate in the online phase under the assumption of a third node generating
correlated randomness in an offline phase. Almost all of the heavy
cryptographic operations are precomputed in an offline phase which
substantially reduces the communication overhead. Chameleon is both scalable
and significantly more efficient than the ABY framework (NDSS'15) it is based
on. Our framework supports signed fixed-point numbers. In particular,
Chameleon's vector dot product of signed fixed-point numbers improves the
efficiency of mining and classification of encrypted data for algorithms based
upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer
convolutional deep neural network shows 133x and 4.2x faster executions than
Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively
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Privacy-preserving clinical decision support system using gaussian kernel-based classification
A clinical decision support system forms a critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Recent trends toward remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health knowledge located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not fully trusted raises possible privacy concerns. In this paper, we propose a novel privacy-preserving protocol for a clinical decision support system where the patients' data always remain in an encrypted form during the diagnosis process. Hence, the server involved in the diagnosis process is not able to learn any extra knowledge about the patient's data and results. Our experimental results on popular medical datasets from UCI-database demonstrate that the accuracy of the proposed protocol is up to 97.21% and the privacy of patient data is not compromised
Machine Learning Models that Remember Too Much
Machine learning (ML) is becoming a commodity. Numerous ML frameworks and
services are available to data holders who are not ML experts but want to train
predictive models on their data. It is important that ML models trained on
sensitive inputs (e.g., personal images or documents) not leak too much
information about the training data.
We consider a malicious ML provider who supplies model-training code to the
data holder, does not observe the training, but then obtains white- or
black-box access to the resulting model. In this setting, we design and
implement practical algorithms, some of them very similar to standard ML
techniques such as regularization and data augmentation, that "memorize"
information about the training dataset in the model yet the model is as
accurate and predictive as a conventionally trained model. We then explain how
the adversary can extract memorized information from the model.
We evaluate our techniques on standard ML tasks for image classification
(CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20
Newsgroups and IMDB). In all cases, we show how our algorithms create models
that have high predictive power yet allow accurate extraction of subsets of
their training data
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