5,473 research outputs found
Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners
The k-nearest neighbors (k-NN) algorithm is a popular and effective
classification algorithm. Due to its large storage and computational
requirements, it is suitable for cloud outsourcing. However, k-NN is often run
on sensitive data such as medical records, user images, or personal
information. It is important to protect the privacy of data in an outsourced
k-NN system.
Prior works have all assumed the data owners (who submit data to the
outsourced k-NN system) are a single trusted party. However, we observe that in
many practical scenarios, there may be multiple mutually distrusting data
owners. In this work, we present the first framing and exploration of privacy
preservation in an outsourced k-NN system with multiple data owners. We
consider the various threat models introduced by this modification. We discover
that under a particularly practical threat model that covers numerous
scenarios, there exists a set of adaptive attacks that breach the data privacy
of any exact k-NN system. The vulnerability is a result of the mathematical
properties of k-NN and its output. Thus, we propose a privacy-preserving
alternative system supporting kernel density estimation using a Gaussian
kernel, a classification algorithm from the same family as k-NN. In many
applications, this similar algorithm serves as a good substitute for k-NN. We
additionally investigate solutions for other threat models, often through
extensions on prior single data owner systems
ToolTalk: Evaluating Tool-Usage in a Conversational Setting
Large language models (LLMs) have displayed massive improvements in reasoning
and decision-making skills and can hold natural conversations with users. Many
recent works seek to augment LLM-based assistants with external tools so they
can access private or up-to-date information and carry out actions on behalf of
users. To better measure the performance of these assistants, this paper
introduces ToolTalk, a benchmark consisting of complex user intents requiring
multi-step tool usage specified through dialogue. ToolTalk contains 28 tools
grouped into 7 plugins, and includes a complete simulated implementation of
each tool, allowing for fully automated evaluation of assistants that rely on
execution feedback. ToolTalk also emphasizes tools that externally affect the
world rather than only tools for referencing or searching information. We
evaluate GPT-3.5 and GPT-4 on ToolTalk resulting in success rates of 26% and
50% respectively. Our analysis of the errors reveals three major categories and
suggests some future directions for improvement. We release ToolTalk at
https://github.com/microsoft/ToolTalk.Comment: 10 pages, 1 figure, ICLR 2024 Submission,
https://github.com/microsoft/ToolTal
A greedy algorithm for dropping digits (Functional Pearl)
Consider the puzzle: given a number, remove digits such that the
resulting number is as large as possible. Various techniques were employed to
derive a linear-time solution to the puzzle: predicate logic was used to
justify the structure of a greedy algorithm, a dependently-typed proof
assistant was used to give a constructive proof of the greedy condition, and
equational reasoning was used to calculate the greedy step as well as the
final, linear-time optimisation
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
We study contextual linear bandit problems under uncertainty on features;
they are noisy with missing entries. To address the challenges from the noise,
we analyze Bayesian oracles given observed noisy features. Our Bayesian
analysis finds that the optimal hypothesis can be far from the underlying
realizability function, depending on noise characteristics, which is highly
non-intuitive and does not occur for classical noiseless setups. This implies
that classical approaches cannot guarantee a non-trivial regret bound. We thus
propose an algorithm aiming at the Bayesian oracle from observed information
under this model, achieving regret bound with respect to
feature dimension and time horizon . We demonstrate the proposed
algorithm using synthetic and real-world datasets.Comment: 30 page
Spinal V2b neurons reveal a role for ipsilateral inhibition in speed control
The spinal cord contains a diverse array of interneurons that govern motor output. Traditionally, models of spinal circuits have emphasized the role of inhibition in enforcing reciprocal alternation between left and right sides or flexors and extensors. However, recent work has shown that inhibition also increases coincident with excitation during contraction. Here, using larval zebrafish, we investigate the V2b (Gata3+) class of neurons, which contribute to flexor-extensor alternation but are otherwise poorly understood. Using newly generated transgenic lines we define two stable subclasses with distinct neurotransmitter and morphological properties. These V2b subclasses synapse directly onto motor neurons with differential targeting to speed-specific circuits. In vivo, optogenetic manipulation of V2b activity modulates locomotor frequency: suppressing V2b neurons elicits faster locomotion, whereas activating V2b neurons slows locomotion. We conclude that V2b neurons serve as a brake on axial motor circuits. Together, these results indicate a role for ipsilateral inhibition in speed control
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