32 research outputs found
Privacy-Preserving Genetic Relatedness Test
An increasing number of individuals are turning to Direct-To-Consumer (DTC)
genetic testing to learn about their predisposition to diseases, traits, and/or
ancestry. DTC companies like 23andme and Ancestry.com have started to offer
popular and affordable ancestry and genealogy tests, with services allowing
users to find unknown relatives and long-distant cousins. Naturally, access and
possible dissemination of genetic data prompts serious privacy concerns, thus
motivating the need to design efficient primitives supporting private genetic
tests. In this paper, we present an effective protocol for privacy-preserving
genetic relatedness test (PPGRT), enabling a cloud server to run relatedness
tests on input an encrypted genetic database and a test facility's encrypted
genetic sample. We reduce the test to a data matching problem and perform it,
privately, using searchable encryption. Finally, a performance evaluation of
hamming distance based PP-GRT attests to the practicality of our proposals.Comment: A preliminary version of this paper appears in the Proceedings of the
3rd International Workshop on Genome Privacy and Security (GenoPri'16
Controlled Data Sharing for Collaborative Predictive Blacklisting
Although sharing data across organizations is often advocated as a promising
way to enhance cybersecurity, collaborative initiatives are rarely put into
practice owing to confidentiality, trust, and liability challenges. In this
paper, we investigate whether collaborative threat mitigation can be realized
via a controlled data sharing approach, whereby organizations make informed
decisions as to whether or not, and how much, to share. Using appropriate
cryptographic tools, entities can estimate the benefits of collaboration and
agree on what to share in a privacy-preserving way, without having to disclose
their datasets. We focus on collaborative predictive blacklisting, i.e.,
forecasting attack sources based on one's logs and those contributed by other
organizations. We study the impact of different sharing strategies by
experimenting on a real-world dataset of two billion suspicious IP addresses
collected from Dshield over two months. We find that controlled data sharing
yields up to 105% accuracy improvement on average, while also reducing the
false positive rate.Comment: A preliminary version of this paper appears in DIMVA 2015. This is
the full version. arXiv admin note: substantial text overlap with
arXiv:1403.212
VERTICES: Efficient Two-Party Vertical Federated Linear Model with TTP-aided Secret Sharing
Vertical Federated Learning (VFL) has emerged as one of the most predominant
approaches for secure collaborative machine learning where the training data is
partitioned by features among multiple parties. Most VFL algorithms primarily
rely on two fundamental privacy-preserving techniques: Homomorphic Encryption
(HE) and secure Multi-Party Computation (MPC). Though generally considered with
stronger privacy guarantees, existing general-purpose MPC frameworks suffer
from expensive computation and communication overhead and are inefficient
especially under VFL settings. This study centers around MPC-based VFL
algorithms and presents a novel approach for two-party vertical federated
linear models via an efficient secret sharing (SS) scheme with a trusted
coordinator. Our approach can achieve significant acceleration of the training
procedure in vertical federated linear models of between 2.5x and 6.6x than
other existing MPC frameworks under the same security setting
VD-PSI : verifiable delegated private set intersection on outsourced private datasets
Private set intersection (PSI) protocols have many real world applications. With the emergence of cloud computing the need arises for PSI protocols on outsourced datasets where the computation is delegated to the cloud. However, due to the possibility of cloud misbehaviors, it is essential to verify the correctness of any delegated computation, and the integrity of any outsourced datasets. Verifiable Computation on private datasets that does not leak any information about the data is very challenging, especially when the datasets are outsourced independently by different clients. In this paper we present VD-PSI, a protocol that allows multiple clients to outsource their private datasets and delegate computation of set intersection to the cloud, while being able to verify the correctness of the result. Clients can independently prepare and upload their datasets, and with their agreement can verifiably delegate the computation of set intersection an unlimited number of times, without the need to download or maintain a local copy of their data. The protocol ensures that the cloud learns nothing about the datasets and the intersection. VD-PSI is efficient as its verification cost is linear to the intersection cardinality, and its computation and communication costs are linear to the dataset cardinality. Also, we provide a formal security analysis in the standard model