6,719 research outputs found
POSTER: Privacy-preserving Indoor Localization
Upcoming WiFi-based localization systems for indoor environments face a
conflict of privacy interests: Server-side localization violates location
privacy of the users, while localization on the user's device forces the
localization provider to disclose the details of the system, e.g.,
sophisticated classification models. We show how Secure Two-Party Computation
can be used to reconcile privacy interests in a state-of-the-art localization
system. Our approach provides strong privacy guarantees for all involved
parties, while achieving room-level localization accuracy at reasonable
overheads.Comment: Poster Session of the 7th ACM Conference on Security & Privacy in
Wireless and Mobile Networks (WiSec'14
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
Federated Large Language Model: A Position Paper
Large scale language models (LLM) have received significant attention and
found diverse applications across various domains, but their development
encounters challenges in real-world scenarios. These challenges arise due to
the scarcity of public domain data availability and the need to maintain
privacy with respect to private domain data. To address these issues, federated
learning (FL) has emerged as a promising technology that enables collaborative
training of shared models while preserving decentralized data. We propose the
concept of federated LLM, which comprises three key components, i.e., federated
LLM pre-training, federated LLM fine-tuning, and federated LLM prompt
engineering. For each component, we discuss its advantage over traditional LLM
training methods and propose specific engineering strategies for
implementation. Furthermore, we explore the novel challenges introduced by the
integration of FL and LLM. We analyze existing solutions and identify potential
obstacles faced by these solutions within the context of federated LLM.Comment: 11 pages, 4 figure
Approximate Two-Party Privacy-Preserving String Matching with Linear Complexity
Consider two parties who want to compare their strings, e.g., genomes, but do
not want to reveal them to each other. We present a system for
privacy-preserving matching of strings, which differs from existing systems by
providing a deterministic approximation instead of an exact distance. It is
efficient (linear complexity), non-interactive and does not involve a third
party which makes it particularly suitable for cloud computing. We extend our
protocol, such that it mitigates iterated differential attacks proposed by
Goodrich. Further an implementation of the system is evaluated and compared
against current privacy-preserving string matching algorithms.Comment: 6 pages, 4 figure
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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