12,037 research outputs found
GraphSE: An Encrypted Graph Database for Privacy-Preserving Social Search
In this paper, we propose GraphSE, an encrypted graph database for online
social network services to address massive data breaches. GraphSE preserves
the functionality of social search, a key enabler for quality social network
services, where social search queries are conducted on a large-scale social
graph and meanwhile perform set and computational operations on user-generated
contents. To enable efficient privacy-preserving social search, GraphSE
provides an encrypted structural data model to facilitate parallel and
encrypted graph data access. It is also designed to decompose complex social
search queries into atomic operations and realise them via interchangeable
protocols in a fast and scalable manner. We build GraphSE with various
queries supported in the Facebook graph search engine and implement a
full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that
GraphSE is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE: An
Encrypted Graph Database for Privacy-Preserving Social Search". It includes
the security proof of the proposed scheme. If you want to cite our work,
please cite the conference version of i
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
GCSA Codes with Noise Alignment for Secure Coded Multi-Party Batch Matrix Multiplication
A secure multi-party batch matrix multiplication problem (SMBMM) is
considered, where the goal is to allow a master to efficiently compute the
pairwise products of two batches of massive matrices, by distributing the
computation across S servers. Any X colluding servers gain no information about
the input, and the master gains no additional information about the input
beyond the product. A solution called Generalized Cross Subspace Alignment
codes with Noise Alignment (GCSA-NA) is proposed in this work, based on
cross-subspace alignment codes. The state of art solution to SMBMM is a coding
scheme called polynomial sharing (PS) that was proposed by Nodehi and
Maddah-Ali. GCSA-NA outperforms PS codes in several key aspects - more
efficient and secure inter-server communication, lower latency, flexible
inter-server network topology, efficient batch processing, and tolerance to
stragglers. The idea of noise alignment can also be combined with N-source
Cross Subspace Alignment (N-CSA) codes and fast matrix multiplication
algorithms like Strassen's construction. Moreover, noise alignment can be
applied to symmetric secure private information retrieval to achieve the
asymptotic capacity
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