Text retrieval enables people to efficiently obtain the desired data from massive text data, so
has become one of the most popular services in information retrieval community. However …

Accurately learning from user data while providing quantifiable privacy guarantees provides
an opportunity to build better ML models while maintaining user trust. This paper presents a …

Balancing the privacy-utility tradeoff is a crucial requirement of many practical machine
learning systems that deal with sensitive customer data. A popular approach for privacy …

Order-preserving encryption (OPE) schemes, whose ciphertexts preserve the natural
ordering of the plaintexts, allow efficient range query processing over outsourced encrypted …

In this work, we focus on protection against identity disclosure in the publication of sparse
multidimensional data. Existing multidimensional anonymization techniquesa) protect the …

Traditional learning-to-rank (LTR) models are usually trained in a centralized approach
based upon a large amount of data. However, with the increasing awareness of data …

Differentially-private mechanisms for text generation typically add carefully calibrated noise
to input words and use the nearest neighbor to the noised input as the output word. When …

Personalized recommendation has demonstrated its effectiveness in improving the problem
of information overload on the Internet. However, evidences show that due to the concerns …

Purpose First, the authors analyze the key problems faced by the protection of digital library
readers' data privacy and behavior privacy. Second, the authors introduce the characteristics …

Personalization is the process of adapting the output of a system to a user's context and
profile. User information such as geographical location, academic and professional …