Browsing by Subject "Homomorphic encryption"

Now showing 1 - 3 of 3

Open Access
Privacy-preserving data sharing and utilization between entities
(2017-07) Demirağ, Didem
In this thesis, we aim to enable privacy-preserving data sharing between entities and propose two systems for this purpose: (i) a veri able computation scheme that enables privacy-preserving similarity computation in the malicious setting and (ii) a privacy-preserving link prediction scheme in the semi-honest setting. Both of these schemes preserve the privacy of the involving parties, while performing some tasks to improve the service quality. In veri able computation, we propose a centralized system, which involves a client and multiple servers. We speci cally focus on the case, in which we want to compute the similarity of a patient's data across several hospitals. Client, who is the hospital that owns the patient data, sends the query to multiple servers, which are di erent hospitals. Client wants to nd similar patients in these hospitals in order to learn about the treatment techniques applied to those patients. In our link prediction scheme, we have two social networks with common users in both of them. We choose two nodes to perform link prediction between them. We perform link prediction in a privacy-preserving way so that neither of the networks learn the structure of the other network. We apply di erent metrics to de ne the similarity of the nodes. While doing this, we utilize privacy-preserving integer comparison.
Open Access
Privacy-preserving protocols for aggregate location queries via homomorphic encryption and multiparty computation
(2019-07) Eryonucu, Cihan
Two main goals of the businesses are to serve their customers better and in the meantime, increase their pro t. One of the ways that businesses can improve their services is using location information of their customers (e.g., positioning their facilities with an objective to minimize the average distance of their customers to their closest facilities). However, without the customer's location data, it is impossible for businesses to achieve such goals. Luckily, in today's world, large amounts of location data is collected by service providers such as telecommunication operators or mobile apps such as Swarm. Service providers are willing to share their data with businesses, doing this will violate the privacy of their customers. Here, we propose two new privacy-preserving schemes for businesses to utilize location data of their customers that is collected by location-based service providers (LBSPs). We utilize lattice based homomorphic encryption and multiparty computation for our new schemes and then we compare them with our existing scheme which is based on partial homomorphic encryption. In our protocols, we hide customer lists of businesses from LBSPs, locations of the customers from the businesses, and query result from LBSPs. In such a setting, we let the businesses send location-based queries to the LBSPs. In addition, we make the query result only available to the businesses and hide them from the LBSPs. We evaluate our proposed schemes to show that they are practical. We then compare our three protocols, discussing each one's advantages and disadvantages and give use cases for all protocols. Our proposed schemes allow data sharing in a private manner and create the foundation for the future complex queries.
Open Access
Threats and solutions for genomic data privacy
(Springer, 2015) Ayday, Erman; Hubaux, J. P.; Gkoulalas-Divani, A.; Loukides, G.
With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists need large collections of human genomes to further increase this speed. Furthermore, individuals are using their genomes to learn about their (genetic) predispositions to diseases, their ancestries, and even their (genetic) compatibilities with potential partners. This trend has also caused the launch of health-related websites and online social networks (OSNs), in which individuals share their genomic data (e.g., OpenSNP or 23andMe). On the other hand, genomic data carries much sensitive information about its owner. By analyzing the DNA of an individual, it is now possible to learn about his disease predispositions (e.g., for Alzheimer’s or Parkinson’s), ancestries, and physical attributes. The threat to genomic privacy is magnified by the fact that a person’s genome is correlated to his family members’ genomes, thus leading to interdependent privacy risks. Thus, in this chapter, focusing on our existing and ongoing work on genomic privacy carried out at EPFL/LCA1, we will first highlight the threats for genomic privacy. Then, we will present the high level descriptions of our solutions to protect the privacy of genomic data and we will discuss future research directions. For a description of the research contributions of other research groups, the reader is referred to Chaps. 16 and 17 of the present volume.