Stochastic Privacy
Authors: Adish Singla, Eric Horvitz, Ece Kamar, Ryen White
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now report on experiments aimed at providing insights on the performance of the stochastic privacy procedures with a case study of the selective collection of user data in support of the personalization of web search. Evaluation to demonstrate the effectiveness of the proposed procedures on a case study of user selection for personalization in web search. |
| Researcher Affiliation | Collaboration | Adish Singla ETH Zurich adish.singla@inf.ethz.ch Eric Horvitz Microsoft Research horvitz@microsoft.com Ece Kamar Microsoft Research eckamar@microsoft.com Ryen White Microsoft Research ryen.white@microsoft.com |
| Pseudocode | Yes | Procedure 1: SPGREEDY |
| Open Source Code | No | The paper provides a link to an extended version PDF ('http://research.microsoft.com/ horvitz/Stochastic Privacy-extended.pdf'), but no explicit statement or link for open-source code for the methodology was found. |
| Open Datasets | No | The primary source of data for the study is obtained from interaction logs on a major web search engine. We consider a fraction of users who issued at least one query in the month of October 2013, restricted to queries coming from IP addresses located within ten neighboring states in the western region of the United States. This results in a pool W of seven million users. No public access information (link, DOI, or formal citation for dataset access) is provided for these specific interaction logs. |
| Dataset Splits | No | The paper describes a 'pool W of seven million users' but does not specify exact training, validation, and test split percentages, sample counts, or a detailed splitting methodology for reproducibility. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments were provided. |
| Software Dependencies | No | The paper mentions using the 'Open Directory Project (ODP)' for classification but does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks) for their implementation. |
| Experiment Setup | No | While the paper describes the experimental application (location-based personalization) and data source, it does not provide specific hyperparameters, optimizer settings, or detailed training configurations for the described algorithms. |