430 research outputs found
EsPRESSo: Efficient Privacy-Preserving Evaluation of Sample Set Similarity
Electronic information is increasingly often shared among entities without
complete mutual trust. To address related security and privacy issues, a few
cryptographic techniques have emerged that support privacy-preserving
information sharing and retrieval. One interesting open problem in this context
involves two parties that need to assess the similarity of their datasets, but
are reluctant to disclose their actual content. This paper presents an
efficient and provably-secure construction supporting the privacy-preserving
evaluation of sample set similarity, where similarity is measured as the
Jaccard index. We present two protocols: the first securely computes the
(Jaccard) similarity of two sets, and the second approximates it, using MinHash
techniques, with lower complexities. We show that our novel protocols are
attractive in many compelling applications, including document/multimedia
similarity, biometric authentication, and genetic tests. In the process, we
demonstrate that our constructions are appreciably more efficient than prior
work.Comment: A preliminary version of this paper was published in the Proceedings
of the 7th ESORICS International Workshop on Digital Privacy Management (DPM
2012). This is the full version, appearing in the Journal of Computer
Securit
Authentication under Constraints
Authentication has become a critical step to gain access to services such as on-line banking, e-commerce, transport systems and cars (contact-less keys). In several cases, however, the authentication process has to be performed under challenging conditions. This thesis is essentially a compendium of five papers which are the result of a two-year study on authentication in constrained settings. The two major constraints considered in this work are: (1) the noise and (2) the computational power. For what concerns authentication under noisy conditions, Paper A and Paper B ad- dress the case in which the noise is in the authentication credentials. More precisely, the aforementioned papers present attacks against biometric authentication systems, that exploit the inherent variant nature of biometric traits to gain information that should not be leaked by the system. Paper C and Paper D study proximity- based authentication, i.e., distance-bounding protocols. In this case, both of the constraints are present: the possible presence of noise in the channel (which affects communication and thus the authentication process), as well as resource constraints on the computational power and the storage space of the authenticating party (called the prover, e.g., an RFID tag). Finally, Paper E investigates how to achieve reliable verification of the authenticity of a digital signature, when the verifying party has limited computational power, and thus offloads part of the computations to an untrusted server. Throughout the presented research work, a special emphasis is given to privacy concerns risen by the constrained conditions
Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud
Biometric recognition, or simply biometrics, is the use of biological
attributes such as face, fingerprints or iris in order to recognize an
individual in an automated manner. A key application of biometrics is
authentication; i.e., using said biological attributes to provide access by
verifying the claimed identity of an individual. This paper presents a
framework for Biometrics-as-a-Service (BaaS) that performs biometric matching
operations in the cloud, while relying on simple and ubiquitous consumer
devices such as smartphones. Further, the framework promotes innovation by
providing interfaces for a plurality of software developers to upload their
matching algorithms to the cloud. When a biometric authentication request is
submitted, the system uses a criteria to automatically select an appropriate
matching algorithm. Every time a particular algorithm is selected, the
corresponding developer is rendered a micropayment. This creates an innovative
and competitive ecosystem that benefits both software developers and the
consumers. As a case study, we have implemented the following: (a) an ocular
recognition system using a mobile web interface providing user access to a
biometric authentication service, and (b) a Linux-based virtual machine
environment used by software developers for algorithm development and
submission
Privacy-preserving query processing over encrypted data in cloud
The query processing of relational data has been studied extensively throughout the past decade. A number of theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, data owners now have the opportunity to outsource not only their data but also data processing functionalities to the cloud. Because of data security and personal privacy concerns, sensitive data (e.g., medical records) should be encrypted before being outsourced to a cloud, and the cloud should perform query processing tasks on the encrypted data only. These tasks are termed as Privacy-Preserving Query Processing (PPQP) over encrypted data. Based on the concept of Secure Multiparty Computation (SMC), SMC-based distributed protocols were developed to allow the cloud to perform queries directly over encrypted data. These protocols protect the confidentiality of the stored data, user queries, and data access patterns from cloud service providers and other unauthorized users. Several queries were considered in an attempt to create a well-defined scope. These queries included the k-Nearest Neighbor (kNN) query, advanced analytical query, and correlated range query. The proposed protocols utilize an additive homomorphic cryptosystem and/or a garbled circuit technique at different stages of query processing to achieve the best performance. In addition, by adopting a multi-cloud computing paradigm, all computations can be done on the encrypted data without using very expensive fully homomorphic encryptions. The proposed protocols\u27 security was analyzed theoretically, and its practicality was evaluated through extensive empirical results --Abstract, page iii
Efficient Verifiable Computation of XOR for Biometric Authentication
This work addresses the security and privacy issues in remotebiometric authentication by proposing an efficient mechanism to verifythe correctness of the outsourced computation in such protocols.In particular, we propose an efficient verifiable computation of XORingencrypted messages using an XOR linear message authenticationcode (MAC) and we employ the proposed scheme to build a biometricauthentication protocol. The proposed authentication protocol is bothsecure and privacy-preserving against malicious (as opposed to honest-but-curious) adversaries. Specifically, the use of the verifiable computation scheme together with an homomorphic encryption protects the privacy of biometric templates against malicious adversaries. Furthermore, in order to achieve unlinkability of authentication attempts, while keeping a low communication overhead, we show how to apply Oblivious RAM and biohashing to our protocol. We also provide a proof of security for the proposed solution. Our simulation results show that the proposed authentication protocol is efficient
PriBioAuth: Privacy-preserving biometric-based remote user authentication
National Research Foundation (NRF) Singapor
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