662 research outputs found
Secure k-Nearest Neighbor Query over Encrypted Data in Outsourced Environments
For the past decade, query processing on relational data has been studied
extensively, and many theoretical and practical solutions to query processing
have been proposed under various scenarios. With the recent popularity of cloud
computing, users now have the opportunity to outsource their data as well as
the data management tasks to the cloud. However, due to the rise of various
privacy issues, sensitive data (e.g., medical records) need to be encrypted
before outsourcing to the cloud. In addition, query processing tasks should be
handled by the cloud; otherwise, there would be no point to outsource the data
at the first place. To process queries over encrypted data without the cloud
ever decrypting the data is a very challenging task. In this paper, we focus on
solving the k-nearest neighbor (kNN) query problem over encrypted database
outsourced to a cloud: a user issues an encrypted query record to the cloud,
and the cloud returns the k closest records to the user. We first present a
basic scheme and demonstrate that such a naive solution is not secure. To
provide better security, we propose a secure kNN protocol that protects the
confidentiality of the data, user's input query, and data access patterns.
Also, we empirically analyze the efficiency of our protocols through various
experiments. These results indicate that our secure protocol is very efficient
on the user end, and this lightweight scheme allows a user to use any mobile
device to perform the kNN query.Comment: 23 pages, 8 figures, and 4 table
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
k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data
Data Mining has wide applications in many areas such as banking, medicine,
scientific research and among government agencies. Classification is one of the
commonly used tasks in data mining applications. For the past decade, due to
the rise of various privacy issues, many theoretical and practical solutions to
the classification problem have been proposed under different security models.
However, with the recent popularity of cloud computing, users now have the
opportunity to outsource their data, in encrypted form, as well as the data
mining tasks to the cloud. Since the data on the cloud is in encrypted form,
existing privacy preserving classification techniques are not applicable. In
this paper, we focus on solving the classification problem over encrypted data.
In particular, we propose a secure k-NN classifier over encrypted data in the
cloud. The proposed k-NN protocol protects the confidentiality of the data,
user's input query, and data access patterns. To the best of our knowledge, our
work is the first to develop a secure k-NN classifier over encrypted data under
the semi-honest model. Also, we empirically analyze the efficiency of our
solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text
overlap with arXiv:1307.482
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