329 research outputs found
Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data
User-generated data is crucial to predictive modeling in many applications.
With a web/mobile/wearable interface, a data owner can continuously record data
generated by distributed users and build various predictive models from the
data to improve their operations, services, and revenue. Due to the large size
and evolving nature of users data, data owners may rely on public cloud service
providers (Cloud) for storage and computation scalability. Exposing sensitive
user-generated data and advanced analytic models to Cloud raises privacy
concerns. We present a confidential learning framework, SecureBoost, for data
owners that want to learn predictive models from aggregated user-generated data
but offload the storage and computational burden to Cloud without having to
worry about protecting the sensitive data. SecureBoost allows users to submit
encrypted or randomly masked data to designated Cloud directly. Our framework
utilizes random linear classifiers (RLCs) as the base classifiers in the
boosting framework to dramatically simplify the design of the proposed
confidential boosting protocols, yet still preserve the model quality. A
Cryptographic Service Provider (CSP) is used to assist the Cloud's processing,
reducing the complexity of the protocol constructions. We present two
constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of
homomorphic encryption, garbled circuits, and random masking to achieve both
security and efficiency. For a boosted model, Cloud learns only the RLCs and
the CSP learns only the weights of the RLCs. Finally, the data owner collects
the two parts to get the complete model. We conduct extensive experiments to
understand the quality of the RLC-based boosting and the cost distribution of
the constructions. Our results show that SecureBoost can efficiently learn
high-quality boosting models from protected user-generated data
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
Improving the Efficiency of Homomorphic Encryption Schemes
In this dissertation, we explore different approaches to practical homomorphic encryption schemes. For partial homomorphic encryption schemes, we observe that the versatility is the main bottleneck. To solve this problem, we propose general approaches to improve versatility of them by either extending the range of supported circuits or extending the message space. These general approaches can be applied to a wide range of partial HE schemes and greatly increase the number of applications that they support. For fully homomorphic encryption schemes, the slow running speed and the large ciphertext are the main challenges. Therefore, we propose efficient implementations as well as methods to compress the ciphertext. In detail, the Gentry Halevi FHE scheme and the LTV FHE scheme are implemented and the resulting performance shows significant improvement over previous works. For ciphertext compression, the concept of scheme conversion is proposed. Given a scheme converter, we can convert between schemes with compact ciphertext for communication and homomorphic schemes for computation
PriBioAuth: Privacy-preserving biometric-based remote user authentication
National Research Foundation (NRF) Singapor
Implementing Homomorphic Encryption Based Secure Feedback Control for Physical Systems
This paper is about an encryption based approach to the secure implementation
of feedback controllers for physical systems. Specifically, Paillier's
homomorphic encryption is used to digitally implement a class of linear dynamic
controllers, which includes the commonplace static gain and PID type feedback
control laws as special cases. The developed implementation is amenable to
Field Programmable Gate Array (FPGA) realization. Experimental results,
including timing analysis and resource usage characteristics for different
encryption key lengths, are presented for the realization of an inverted
pendulum controller; as this is an unstable plant, the control is necessarily
fast
- …