13,350 research outputs found
An Audit Logic for Accountability
We describe and implement a policy language. In our system, agents can
distribute data along with usage policies in a decentralized architecture. Our
language supports the specification of conditions and obligations, and also the
possibility to refine policies. In our framework, the compliance with usage
policies is not actively enforced. However, agents are accountable for their
actions, and may be audited by an authority requiring justifications.Comment: To appear in Proceedings of IEEE Policy 200
Interactive and Concentrated Differential Privacy for Bandits
Bandits play a crucial role in interactive learning schemes and modern
recommender systems. However, these systems often rely on sensitive user data,
making privacy a critical concern. This paper investigates privacy in bandits
with a trusted centralized decision-maker through the lens of interactive
Differential Privacy (DP). While bandits under pure -global DP have
been well-studied, we contribute to the understanding of bandits under zero
Concentrated DP (zCDP). We provide minimax and problem-dependent lower bounds
on regret for finite-armed and linear bandits, which quantify the cost of
-global zCDP in these settings. These lower bounds reveal two hardness
regimes based on the privacy budget and suggest that -global zCDP
incurs less regret than pure -global DP. We propose two -global
zCDP bandit algorithms, AdaC-UCB and AdaC-GOPE, for finite-armed and linear
bandits respectively. Both algorithms use a common recipe of Gaussian mechanism
and adaptive episodes. We analyze the regret of these algorithms to show that
AdaC-UCB achieves the problem-dependent regret lower bound up to multiplicative
constants, while AdaC-GOPE achieves the minimax regret lower bound up to
poly-logarithmic factors. Finally, we provide experimental validation of our
theoretical results under different settings
Improved quantum data analysis
We provide more sample-efficient versions of some basic routines in quantum
data analysis, along with simpler proofs. Particularly, we give a quantum
"Threshold Search" algorithm that requires only
samples of a -dimensional state . That is, given observables such that for at
least one , the algorithm finds with . As a consequence, we obtain a Shadow Tomography algorithm
requiring only samples, which
simultaneously achieves the best known dependence on each parameter , ,
. This yields the same sample complexity for quantum Hypothesis
Selection among states; we also give an alternative Hypothesis Selection
method using samples
A Novel Privacy Preserving Search Technique for Stego Data in Untrusted Cloud
We propose the first privacy preserving search technique for stego health data in untrusted cloud in this paper. The Cloud computing is a popular technology to the healthcare providers for outsourcing health data due to flexibility and cost effectiveness. However, outsourcing health data to the cloud introduces serious privacy issues to the patient. For example, dishonest personnel of the cloud provider may disclose patient sensitive information to business organizations for some financial benefits. Using steganography, patient sensitive information is hidden within health data for privacy preservation. As a result, stego health data is generated. To the best of our knowledge, no method exists for searching a particular stego data without disclosing any information to the cloud. We propose a framework for privacy preserving search over stego health data. We systematically describe each component of the proposed framework. We conduct several experiments to evaluate the performance of the framework
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