Privacy-Preserving Computation over Genetic Data: HLA Matching and so on

Genetic data is an indispensable part of big data, promoting the advancement of life science and biomedicine. Yet, highly private genetic data also brings concerns about privacy risks in data shar- ing. In our work, we adopt the cryptographic prim- itive Secure Function Evaluation (SFE) to address this problem. A secure SFE scheme allows insti- tutions and hospitals to compute a function while preserving the privacy of their input data, and each participant knows nothing but their own input and the final result. In our work, we present privacy-preserving solutions for Human Leukocyte Antigen (HLA) matching and two popular biostatistics tests: Chi-squared test and odds ratio test. We also show that our protocols are compatible with multiple databases simultaneously and could feasibly han- dle larger-scale data up to genome-wide level. This approach may serve as a new way to jointly analyze distributed and restricted genetic data among insti- tutions and hospitals. Meanwhile, it can potentially be extended to other genetic analysis algorithms, allowing individuals to analyze their own genomes without endangering data privacy

Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing

In this paper, we propose a privacy preserving distributed clustering protocol for horizontally partitioned data based on a very efficient homomorphic additive secret sharing scheme. The model we use for the protocol is novel in the sense that it utilizes two non-colluding third parties. We provide a brief security analysis of our protocol from information theoretic point of view, which is a stronger security model. We show communication and computation complexity analysis of our protocol along with another protocol previously proposed for the same problem. We also include experimental results for computation and communication overhead of these two protocols. Our protocol not only outperforms the others in execution time and communication overhead on data holders, but also uses a more efficient model for many data mining applications

Secret charing vs. encryption-based techniques for privacy preserving data mining

Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach