13,919 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
Privacy-preserving Cross-domain Routing Optimization -- A Cryptographic Approach
Today's large-scale enterprise networks, data center networks, and wide area
networks can be decomposed into multiple administrative or geographical
domains. Domains may be owned by different administrative units or
organizations. Hence protecting domain information is an important concern.
Existing general-purpose Secure Multi-Party Computation (SMPC) methods that
preserves privacy for domains are extremely slow for cross-domain routing
problems. In this paper we present PYCRO, a cryptographic protocol specifically
designed for privacy-preserving cross-domain routing optimization in Software
Defined Networking (SDN) environments. PYCRO provides two fundamental routing
functions, policy-compliant shortest path computing and bandwidth allocation,
while ensuring strong protection for the private information of domains. We
rigorously prove the privacy guarantee of our protocol. We have implemented a
prototype system that runs PYCRO on servers in a campus network. Experimental
results using real ISP network topologies show that PYCRO is very efficient in
computation and communication costs
InShopnito: an advanced yet privacy-friendly mobile shopping application
Mobile Shopping Applications (MSAs) are rapidly gaining popularity. They enhance the shopping experience, by offering customized recommendations or incorporating customer loyalty programs. Although MSAs are quite effective at attracting new customers and binding existing ones to a retailer's services, existing MSAs have several shortcomings. The data collection practices involved in MSAs and the lack of transparency thereof are important concerns for many customers. This paper presents inShopnito, a privacy-preserving mobile shopping application. All transactions made in inShopnito are unlinkable and anonymous. However, the system still offers the expected features from a modern MSA. Customers can take part in loyalty programs and earn or spend loyalty points and electronic vouchers. Furthermore, the MSA can suggest personalized recommendations even though the retailer cannot construct rich customer profiles. These profiles are managed on the smartphone and can be partially disclosed in order to get better, customized recommendations. Finally, we present an implementation called inShopnito, of which the security and performance is analyzed. In doing so, we show that it is possible to have a privacy-preserving MSA without having to sacrifice practicality
Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters
Data privacy is crucial when dealing with biometric data. Accounting for the
latest European data privacy regulation and payment service directive,
biometric template protection is essential for any commercial application.
Ensuring unlinkability across biometric service operators, irreversibility of
leaked encrypted templates, and renewability of e.g., voice models following
the i-vector paradigm, biometric voice-based systems are prepared for the
latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean
and cosine comparators are known to ensure data privacy demands, without loss
of discrimination nor calibration performance. Bridging gaps from template
protection to speaker recognition, two architectures are proposed for the
two-covariance comparator, serving as a generative model in this study. The
first architecture preserves privacy of biometric data capture subjects. In the
second architecture, model parameters of the comparator are encrypted as well,
such that biometric service providers can supply the same comparison modules
employing different key pairs to multiple biometric service operators. An
experimental proof-of-concept and complexity analysis is carried out on the
data from the 2013-2014 NIST i-vector machine learning challenge
An Expressive Model for the Web Infrastructure: Definition and Application to the BrowserID SSO System
The web constitutes a complex infrastructure and as demonstrated by numerous
attacks, rigorous analysis of standards and web applications is indispensable.
Inspired by successful prior work, in particular the work by Akhawe et al. as
well as Bansal et al., in this work we propose a formal model for the web
infrastructure. While unlike prior works, which aim at automatic analysis, our
model so far is not directly amenable to automation, it is much more
comprehensive and accurate with respect to the standards and specifications. As
such, it can serve as a solid basis for the analysis of a broad range of
standards and applications.
As a case study and another important contribution of our work, we use our
model to carry out the first rigorous analysis of the BrowserID system (a.k.a.
Mozilla Persona), a recently developed complex real-world single sign-on system
that employs technologies such as AJAX, cross-document messaging, and HTML5 web
storage. Our analysis revealed a number of very critical flaws that could not
have been captured in prior models. We propose fixes for the flaws, formally
state relevant security properties, and prove that the fixed system in a
setting with a so-called secondary identity provider satisfies these security
properties in our model. The fixes for the most critical flaws have already
been adopted by Mozilla and our findings have been rewarded by the Mozilla
Security Bug Bounty Program.Comment: An abridged version appears in S&P 201
Privacy-Aware Processing of Biometric Templates by Means of Secure Two-Party Computation
The use of biometric data for person identification and access control is gaining more and more popularity. Handling biometric data, however, requires particular care, since biometric data is indissolubly tied to the identity of the owner hence raising important security and privacy issues. This chapter focuses on the latter, presenting an innovative approach that, by relying on tools borrowed from Secure Two Party Computation (STPC) theory, permits to process the biometric data in encrypted form, thus eliminating any risk that private biometric information is leaked during an identification process. The basic concepts behind STPC are reviewed together with the basic cryptographic primitives needed to achieve privacy-aware processing of biometric data in a STPC context. The two main approaches proposed so far, namely homomorphic encryption and garbled circuits, are discussed and the way such techniques can be used to develop a full biometric matching protocol described. Some general guidelines to be used in the design of a privacy-aware biometric system are given, so as to allow the reader to choose the most appropriate tools depending on the application at hand
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