Evidence map›Paper›PMID 32803135›Full record

ArticleIEEE letters of the Computer Society

A Guide for Private Outlier Analysis.

Hafiz Asif, Periklis A Papakonstantinou, Jaideep Vaidya

Abstract read
In one paragraph

Article in IEEE letters of the Computer Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Identifying Anomalies while Preserving Privacy.IEEE transactions on knowledge and data engineering · 2023
    Article
  2. Identification of a Glutamatergic Claustrum-Anterior Cingulate Cortex Circuit for Visceral Pain Processing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hafiz AsifMSIS Department, Rutgers University, USA.
Periklis A PapakonstantinouMSIS Department, Rutgers University, USA.
Jaideep VaidyaMSIS Department, Rutgers University, USA.

Funding

Supplement for high performance compute clustersR35GM134927 · NIGMS · RUTGERS THE STATE UNIV OF NJ NEWARK · PI VAIDYA, JAIDEEP · 2020 to 2024
$2.1M
Secure and PrivAte Collaborative EnvironmentS (SPACES) for biomedical analyticsR01GM118574 · NIGMS · RUTGERS THE STATE UNIV OF NJ NEWARK · PI VAIDYA, JAIDEEP · 2017 to 2019
$1.1M
NIGMS NIH HHS R01 GM118574NIGMS NIH HHS R35 GM134927
6 · The paper itself

Abstract

The increasing societal demand for data privacy has led researchers to develop methods to preserve privacy in data analysis. However, outlier analysis, a fundamental data analytics task with critical applications in medicine, finance, and national security, has only been analyzed for a few specialized cases of data privacy. This work is the first to provide a general framework for private outlier analysis, which is a two-step process. First, we show how to identify the relevant problem-specifications and then provide a practical solution that formally meets these specifications.

Indexed as

anomalydifferential privacyoutliersprivacysecuritysensitive privacy

Identifiers

PMID32803135
PMCPMC7423021

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.