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Homomorphic Encrypted Revenue Management

https://doi.org/10.2139/ssrn.4724820
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-48910-x_16openalex-extracted
Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
inferredcites →https://doi.org/10.1007/978-3-030-34339-2_9openalex-extracted
Secure Best Arm Identification in Multi-armed Bandits
inferredcites →https://doi.org/10.1007/978-3-030-39445-5_59openalex-extracted
Design and Implementation of an e-Voting System Based on Paillier Encryption
inferredcites →https://doi.org/10.1007/978-3-030-62576-4_13openalex-extracted
Secure Cumulative Reward Maximization in Linear Stochastic Bandits
inferredcites →https://doi.org/10.1007/978-3-319-70694-8_15openalex-extracted
Homomorphic Encryption for Arithmetic of Approximate Numbers
inferredcites →https://doi.org/10.1007/978-3-540-45126-6_17openalex-extracted
Secure Generalized Vickrey Auction Using Homomorphic Encryption
inferredcites →https://doi.org/10.1090/s0002-9947-1937-1501905-7openalex-extracted
Applications of the theory of Boolean rings to general topology
inferredcites →https://doi.org/10.1090/s0025-5718-66-99922-4openalex-extracted
A Note on an Iterative Method for Generalized Inversion of Matrices
inferredcites →https://doi.org/10.1109/ispass61541.2024.00016openalex-extracted
CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption
inferredcites →https://doi.org/10.1109/sp.2013.30openalex-extracted
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records
inferredcites →https://doi.org/10.1109/tetc.2020.3014636openalex-extracted
Towards the AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data With GPUs
inferredcites →https://doi.org/10.1137/22m1522711openalex-extracted
Tangent Space and Dimension Estimation with the Wasserstein Distance
inferredcites →https://doi.org/10.1145/1568318.1568324openalex-extracted
On lattices, learning with errors, random linear codes, and cryptography
inferredcites →https://doi.org/10.1145/3131672.3131697openalex-extracted
Secure Sharing of Partially Homomorphic Encrypted IoT Data
inferredcites →https://doi.org/10.1145/3335741openalex-extracted
Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio Micali
inferredcites →https://doi.org/10.1145/3418290openalex-extracted
DP-cryptography
inferredcites →https://doi.org/10.1145/3474366.3486923openalex-extracted
Pyfhel
inferredcites →https://doi.org/10.1145/3605759.3625258openalex-extracted
A Probabilistic Design for Practical Homomorphic Majority Voting with Intrinsic Differential Privacy
inferredcites →https://doi.org/10.1201/9781351133036openalex-extracted
Introduction to Modern Cryptography
inferredcites →https://doi.org/10.1287/mnsc.2019.3485openalex-extracted
Feature-Based Dynamic Pricing
inferredcites →https://doi.org/10.1287/mnsc.2020.3671openalex-extracted
Dynamic Pricing (and Assortment) Under a Static Calendar
inferredcites →https://doi.org/10.1287/mnsc.2020.3680openalex-extracted
Personalized Dynamic Pricing with Machine Learning: High-Dimensional Features and Heterogeneous Elasticity
inferredcites →https://doi.org/10.1287/mnsc.2020.3772openalex-extracted
A Statistical Learning Approach to Personalization in Revenue Management
inferredcites →https://doi.org/10.1287/mnsc.2021.4129openalex-extracted
Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
inferredcites →https://doi.org/10.1287/mnsc.2022.4513openalex-extracted
Digital Privacy
inferredcites →https://doi.org/10.1287/mnsc.2023.4725openalex-extracted
How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time
inferredcites →https://doi.org/10.1287/mnsc.2023.4925openalex-extracted
Privacy-Preserving Personalized Revenue Management
inferredcites →https://doi.org/10.1287/mnsc.2023.4939openalex-extracted
Price Delegation with Learning Agents
inferredcites →https://doi.org/10.1287/msom.2022.1130openalex-extracted
Privacy Management in Service Systems
inferredcites →https://doi.org/10.1287/msom.2023.0271openalex-extracted
Privacy-Preserving Personalized Recommender Systems
inferredcites →https://doi.org/10.1287/opre.2014.1294openalex-extracted
Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-Myopic Policies
inferredcites →https://doi.org/10.1287/opre.2020.2021openalex-extracted
Private Sequential Learning
inferredcites →https://doi.org/10.1287/opre.2022.0014openalex-extracted
Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms
inferredcites →https://doi.org/10.1561/0400000042openalex-extracted
The Algorithmic Foundations of Differential Privacy
inferredcites →https://doi.org/10.2139/ssrn.2765257openalex-extracted
Dynamic Pricing with Demand Covariates
inferredcites →https://doi.org/10.2139/ssrn.2855843openalex-extracted
Dynamic Pricing in High-Dimensions
inferredcites →https://doi.org/10.2139/ssrn.3912642openalex-extracted
Consumer Privacy in Online Retail Supply Chains
inferredcites →https://doi.org/10.2139/ssrn.4488404openalex-extracted
Data Privacy in Pricing: Estimation Bias and Implications
inferredcites →https://doi.org/10.2139/ssrn.4723813openalex-extracted
The Limits of Price Discrimination Under Privacy Constraints
inferredcites →https://doi.org/10.48550/arxiv.1412.6071openalex-extracted
Fractional Max-Pooling
inferredcites →https://doi.org/10.48550/arxiv.1703.00839openalex-extracted
Encrypted accelerated least squares regression
inferredcites →https://doi.org/10.48550/arxiv.1810.12380openalex-extracted
Conditionals in Homomorphic Encryption and Machine Learning Applications
inferredcites →https://doi.org/10.48550/arxiv.1909.09836openalex-extracted
Optimal query complexity for private sequential learning against eavesdropping
inferredcites →https://doi.org/10.48550/arxiv.2004.08887openalex-extracted
DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications
inferredcites →https://doi.org/10.48550/arxiv.2009.12920openalex-extracted
Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
inferredcites →https://doi.org/10.48550/arxiv.2109.04615openalex-extracted
Differential Privacy in Personalized Pricing with Nonparametric Demand Models
inferredcites →https://doi.org/10.48550/arxiv.2201.03968openalex-extracted
Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms
inferredcites →https://doi.org/10.5555/3322706.3322715openalex-extracted
Dynamic pricing in high-dimensions
inferredcites →https://doi.org/10.6028/nist.sp.800-226openalex-extracted
Guidelines for evaluating differential privacy guarantees
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