Privacy-preserving Maximum Likelihood Estimation for Distributed Data

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Privacy-Preserving Maximum Likelihood Estimation for Distributed Data

Recent technological advances enable the collection of huge amounts of data. Commonly, these data are generated, stored, and owned by multiple entities that are unwilling to cede control of their data. This distributed environment requires statistical tools that can produce correct results while preserving data privacy. Privacy-preserving protocols have been proposed to solve specific statistic...

متن کامل

Robust distributed maximum likelihood estimation with dependent quantized data

In this paper, distributed maximum likelihood estimation (MLE) with quantized data is considered under the assumption that the structure of the joint probability density function (pdf) is known, but it contains unknown deterministic parameters. The parameters may include different vector parameters corresponding to marginal pdfs and parameters that describe dependence of observations across sen...

متن کامل

Distributed Privacy Preserving Data Collection

We study the distributed privacy preserving data collection problem: an untrusted data collector (e.g., a medical research institute) wishes to collect data (e.g., medical records) from a group of respondents (e.g., patients). Each respondent owns a multi-attributed record which contains both non-sensitive (e.g., quasi-identifiers) and sensitive information (e.g., a particular disease), and sub...

متن کامل

Tools for Privacy Preserving Distributed Data Mining

Privacy preserving mining of distributed data has numerous applications. Each application poses different constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be combined for specific privacy-preserving da...

متن کامل

Maximum Likelihood Estimation for Lossy Data Compression∗

In lossless data compression, given a sequence of observations (Xn)n≥1 and a family of probability distributions {Qθ}θ∈Θ, the estimators (θ̃n)n≥1 obtained by minimizing the ideal Shannon code-lengths over the family {Qθ}θ∈Θ, θ̃n := arg min θ∈Θ [ − logQθ(X 1 ) ] , whereXn 1 := (X1, X2, . . . , Xn), coincide with the classical maximum-likelihood estimators (MLEs). In the corresponding lossy compres...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of Privacy and Confidentiality

سال: 2010

ISSN: 2575-8527

DOI: 10.29012/jpc.v1i2.574