Secure equality testing protocols in the two-party setting

Protocols for securely testing the equality of two encrypted integers are common building blocks for a number of proposals in the literature that aim for privacy preservation. Being used repeatedly in many cryptographic protocols, designing efficient equality testing protocols is important in terms of computation and communication overhead. In...

Improved privacy of dynamic group services

We consider dynamic group services, where outputs based on small samples of privacy-sensitive user inputs are repetitively computed. The leakage of user input data is analysed, caused by producing multiple outputs, resulting from inputs of frequently changing sets of users. A cryptographic technique, known as random user selection, is...

Secure comparison protocols in the semi-honest model

Due to high complexity, comparison protocols with secret inputs have been a bottleneck in the design of privacy-preserving cryptographic protocols. Different solutions based on homomorphic encryption, garbled circuits and secret sharing techniques have been proposed over the last few years, each claiming high efficiency. Unfortunately, a fair...

Privacy enhanced personal services for smart grids

Millions of people are now increasingly using smart devices at home to improve the quality of their lives. Unfortunately, the smart devices that we use at home for comfort and sim-plicity make our lives more complicated in terms of man-agement due to several issues like mismatching interfaces and complexity of the micro-management. One approach...

Privacy-preserving recommender systems in dynamic environments

Recommender systems play a crucial role today in on-line applications as they improve the customer satisfaction, and at the same time results in an increase in the profit for the service provider. However, there are serious privacy concerns as such systems rely on the personal data of the customers. There have been several proposals to provide...

Privacy-preserving distributed clustering

Clustering is a very important tool in data mining and is widely used in on-line services for medical, financial and social environments. The main goal in clustering is to create sets of similar objects in a data set. The data set to be used for clustering can be owned by a single entity, or in some cases, information from different databases is...

Generating private recommendations efficiently using homomorphic encryption and data packing

Recommender systems have become an important tool for personalization of online services. Generating recommendations in online services depends on privacy-sensitive data collected from the users. Traditional data protection mechanisms focus on access control and secure transmission, which provide security only against malicious third parties,...

Privacy-Preserving Content-Based Recommender System

By offering personalized content to users, recommender systems have become a vital tool in e-commerce and online media applications. Content-based algorithms recommend items or products to users, that are most similar to those previously purchased or consumed. Unfortunately, collecting and storing ratings, on which content-based methods rely,...

Privacy-Preserving Content-Based Recommendations through Homomorphic Encryption

By offering personalized content to users, recommender systems have become a vital tool in ecommerce and online media applications. Content-based algorithms recommend items or products to users, that are most similar to those previously purchased or consumed. Unfortunately, collecting and storing ratings, on which content-based methods rely,...

Generating private recommendations in a social trust network

Recommender systems have become increasingly important in e-commerce as they can guide customers with finding personalized services and products. A variant of recommender systems that generates recommendations from a set of trusted people is recently getting more attention in social networks. However, people are concerned about their privacy as...

Efficiently Computing Private Recommendations

Online recommender systems enable personalized service to users. The underlying collaborative filtering techniques operate on privacy sensitive user data, which could be misused by the service provider. To protect user privacy, we propose to encrypt the data and generate recommendations by processing them under encryption. Thus, the service...

Privacy Enhanced Recommender System

Recommender systems are widely used in online applications since they enable personalized service to the users. The underlying collaborative filtering techniques work on user’s data which are mostly privacy sensitive and can be misused by the service provider. To protect the privacy of the users, we propose to encrypt the privacy sensitive data...

Efficiently Computing Private Recommendations

Online recommender systems enable personalized service to users. The underlying collaborative filtering techniques operate on privacy sensitive user data, which could be misused if it is leaked or by the service provider him self. To protect user’s privacy, we propose to encrypt the data and generate recommendations by processing them under...

Privacy-preserving user clustering in a social network

In a ubiquitously connected world, social networks are playing an important role on the Internet by allowing users to find groups of people with similar interests. The data needed to construct such networks may be considered sensitive personal information by the users, which raises privacy concerns. The problem of building social networks while...