880 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
Data-Oblivious Graph Algorithms in Outsourced External Memory
Motivated by privacy preservation for outsourced data, data-oblivious
external memory is a computational framework where a client performs
computations on data stored at a semi-trusted server in a way that does not
reveal her data to the server. This approach facilitates collaboration and
reliability over traditional frameworks, and it provides privacy protection,
even though the server has full access to the data and he can monitor how it is
accessed by the client. The challenge is that even if data is encrypted, the
server can learn information based on the client data access pattern; hence,
access patterns must also be obfuscated. We investigate privacy-preserving
algorithms for outsourced external memory that are based on the use of
data-oblivious algorithms, that is, algorithms where each possible sequence of
data accesses is independent of the data values. We give new efficient
data-oblivious algorithms in the outsourced external memory model for a number
of fundamental graph problems. Our results include new data-oblivious
external-memory methods for constructing minimum spanning trees, performing
various traversals on rooted trees, answering least common ancestor queries on
trees, computing biconnected components, and forming open ear decompositions.
None of our algorithms make use of constant-time random oracles.Comment: 20 page
SoK: Cryptographically Protected Database Search
Protected database search systems cryptographically isolate the roles of
reading from, writing to, and administering the database. This separation
limits unnecessary administrator access and protects data in the case of system
breaches. Since protected search was introduced in 2000, the area has grown
rapidly; systems are offered by academia, start-ups, and established companies.
However, there is no best protected search system or set of techniques.
Design of such systems is a balancing act between security, functionality,
performance, and usability. This challenge is made more difficult by ongoing
database specialization, as some users will want the functionality of SQL,
NoSQL, or NewSQL databases. This database evolution will continue, and the
protected search community should be able to quickly provide functionality
consistent with newly invented databases.
At the same time, the community must accurately and clearly characterize the
tradeoffs between different approaches. To address these challenges, we provide
the following contributions:
1) An identification of the important primitive operations across database
paradigms. We find there are a small number of base operations that can be used
and combined to support a large number of database paradigms.
2) An evaluation of the current state of protected search systems in
implementing these base operations. This evaluation describes the main
approaches and tradeoffs for each base operation. Furthermore, it puts
protected search in the context of unprotected search, identifying key gaps in
functionality.
3) An analysis of attacks against protected search for different base
queries.
4) A roadmap and tools for transforming a protected search system into a
protected database, including an open-source performance evaluation platform
and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac
The Secure Link Prediction Problem
Link Prediction is an important and well-studied problem for social networks.
Given a snapshot of a graph, the link prediction problem predicts which new
interactions between members are most likely to occur in the near future. As
networks grow in size, data owners are forced to store the data in remote cloud
servers which reveals sensitive information about the network. The graphs are
therefore stored in encrypted form.
We study the link prediction problem on encrypted graphs. To the best of our
knowledge, this secure link prediction problem has not been studied before. We
use the number of common neighbors for prediction. We present three algorithms
for the secure link prediction problem. We design prototypes of the schemes and
formally prove their security. We execute our algorithms in real-life datasets.Comment: This has been accepted for publication in Advances in Mathematics of
Communications (AMC) journa
Statistically-secure ORAM with Overhead
We demonstrate a simple, statistically secure, ORAM with computational
overhead ; previous ORAM protocols achieve only
computational security (under computational assumptions) or require
overheard. An additional benefit of our ORAM is its
conceptual simplicity, which makes it easy to implement in both software and
(commercially available) hardware.
Our construction is based on recent ORAM constructions due to Shi, Chan,
Stefanov, and Li (Asiacrypt 2011) and Stefanov and Shi (ArXiv 2012), but with
some crucial modifications in the algorithm that simplifies the ORAM and enable
our analysis. A central component in our analysis is reducing the analysis of
our algorithm to a "supermarket" problem; of independent interest (and of
importance to our analysis,) we provide an upper bound on the rate of "upset"
customers in the "supermarket" problem
Shortest Path Computation with No Information Leakage
Shortest path computation is one of the most common queries in location-based
services (LBSs). Although particularly useful, such queries raise serious
privacy concerns. Exposing to a (potentially untrusted) LBS the client's
position and her destination may reveal personal information, such as social
habits, health condition, shopping preferences, lifestyle choices, etc. The
only existing method for privacy-preserving shortest path computation follows
the obfuscation paradigm; it prevents the LBS from inferring the source and
destination of the query with a probability higher than a threshold. This
implies, however, that the LBS still deduces some information (albeit not
exact) about the client's location and her destination. In this paper we aim at
strong privacy, where the adversary learns nothing about the shortest path
query. We achieve this via established private information retrieval
techniques, which we treat as black-box building blocks. Experiments on real,
large-scale road networks assess the practicality of our schemes.Comment: VLDB201
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