Add to ORKGInternational audienceSimilarity search in high dimensional space database is split into two worlds: i) fast, scalable, and approximate search algorithms which are not secure, and ii) search protocols based on secure computation which are not scalable. This paper presents a one-way privacy protocol that lies in between these two worlds. Approximate metrics for the cosine similarity allows speed. Elements of large random matrix theory provides security evidences if the size of the database is not too big with respect to the space dimension
We propose a similarity index that ensures data privacy and thus is suitable for search systems outs...
As databases increasingly integrate different types of information such as time-series, multimedia a...
We study two information similarity measures, relative entropy and the similarity metric, and method...
International audienceSimilarity search in high dimensional space database is split into two worlds:...
International audienceBig data systems are gathering more and more information in order to discover ...
International audienceWe study an indexing architecture to store and search in a database of high-di...
Abstract — In recent years, due to the appealing features of cloud computing, large amount of data h...
The ability to handle noisy or imprecise data is becoming increasingly important in computing. In th...
Nearest neighbor search is a fundamental building-block for a wide range of applications. A privacy-...
The past few decades have witnessed considerable efforts for achieving a Privacy- Preserving Computi...
To enable efficient similarity search in large databases, many indexing techniques use a linear tran...
In this thesis, we study high dimensional approximate similarity search algorithms. High dimensional...
International audienceThis paper presents a moderately secure but very efficient approximate nearest...
Given a sparse binary matrix A and a sparse query vector x, can we efficiently identify the large en...
We present one of the main problems in information retrieval and data mining, which is the similarit...
We propose a similarity index that ensures data privacy and thus is suitable for search systems outs...
As databases increasingly integrate different types of information such as time-series, multimedia a...
We study two information similarity measures, relative entropy and the similarity metric, and method...
International audienceSimilarity search in high dimensional space database is split into two worlds:...
International audienceBig data systems are gathering more and more information in order to discover ...
International audienceWe study an indexing architecture to store and search in a database of high-di...
Abstract — In recent years, due to the appealing features of cloud computing, large amount of data h...
The ability to handle noisy or imprecise data is becoming increasingly important in computing. In th...
Nearest neighbor search is a fundamental building-block for a wide range of applications. A privacy-...
The past few decades have witnessed considerable efforts for achieving a Privacy- Preserving Computi...
To enable efficient similarity search in large databases, many indexing techniques use a linear tran...
In this thesis, we study high dimensional approximate similarity search algorithms. High dimensional...
International audienceThis paper presents a moderately secure but very efficient approximate nearest...
Given a sparse binary matrix A and a sparse query vector x, can we efficiently identify the large en...
We present one of the main problems in information retrieval and data mining, which is the similarit...
We propose a similarity index that ensures data privacy and thus is suitable for search systems outs...
As databases increasingly integrate different types of information such as time-series, multimedia a...
We study two information similarity measures, relative entropy and the similarity metric, and method...