Add to ORKGWe present a method for performing linear regression on the union of distributed databases that does not entail constructing an integrated database, and therefore preserves confidentiality of the individual databases. The method can be used by statistical agencies to share information from their individual databases, or to make such information available to others
Statistical disclosure control (SDC) methods aim to protect privacy of the confidential information ...
In this work, we present solutions for technical difficulties in deploying secure multi-party comput...
We propose a protocol for secure mining of association rules in horizontally distributed databases. ...
We present several methods for performing linear regression on the union of distributed databases th...
We present a method for performing statistical valid linear regressions on the union of distributed ...
Reluctance of statistical agencies and other data owners to share possibly confidential or proprieta...
Abstract. The machine learning community has focused on confiden-tiality problems associated with st...
Privacy-preserving data mining (PPDM) techniques aim to construct efficient data mining algorithms w...
Preserving the privacy of individual databases when carrying out statistical calculations has a long...
We propose privacy-preserving protocols for computing linear regression models, in the setting where...
Consider a network of k parties, each holding a long sequence of n entries (a database), with minimu...
Abstract. Regression is arguably the most applied data analysis method. Today there are many scenari...
Linear regression is an important statistical tool that models the relationship between some explana...
In recent years there has been massive progress in the development of technologies for storing and p...
Let us consider the following situation: t entities (e.g., hospitals) hold different databases conta...
Statistical disclosure control (SDC) methods aim to protect privacy of the confidential information ...
In this work, we present solutions for technical difficulties in deploying secure multi-party comput...
We propose a protocol for secure mining of association rules in horizontally distributed databases. ...
We present several methods for performing linear regression on the union of distributed databases th...
We present a method for performing statistical valid linear regressions on the union of distributed ...
Reluctance of statistical agencies and other data owners to share possibly confidential or proprieta...
Abstract. The machine learning community has focused on confiden-tiality problems associated with st...
Privacy-preserving data mining (PPDM) techniques aim to construct efficient data mining algorithms w...
Preserving the privacy of individual databases when carrying out statistical calculations has a long...
We propose privacy-preserving protocols for computing linear regression models, in the setting where...
Consider a network of k parties, each holding a long sequence of n entries (a database), with minimu...
Abstract. Regression is arguably the most applied data analysis method. Today there are many scenari...
Linear regression is an important statistical tool that models the relationship between some explana...
In recent years there has been massive progress in the development of technologies for storing and p...
Let us consider the following situation: t entities (e.g., hospitals) hold different databases conta...
Statistical disclosure control (SDC) methods aim to protect privacy of the confidential information ...
In this work, we present solutions for technical difficulties in deploying secure multi-party comput...
We propose a protocol for secure mining of association rules in horizontally distributed databases. ...