Protecting and evaluating genomic privacy in medical tests and personalized medicine

Abstract

In this paper, we propose privacy-enhancing technologies for medical tests and personalized medicine methods that use patients' genomic data. Focusing on genetic diseasesusceptibility tests, we develop a new architecture (between the patient and the medical unit) and propose a "privacypreserving disease susceptibility test" (PDS) by using homomorphic encryption and proxy re-encryption. Assuming the whole genome sequencing to be done by a certified institution, we propose to store patients' genomic data encrypted by their public keys at a "storage and processing unit" (SPU). Our proposed solution lets the medical unit retrieve the encrypted genomic data from the SPU and process it for medical tests and personalized medicine methods, while preserving the privacy of patients' genomic data. We also quantify the genomic privacy of a patient (from the medical unit's point of view) and show how a patient's genomic privacy decreases with the genetic tests he undergoes due to (i) the nature of the genetic test, and (ii) the characteristics of the genomic data. Furthermore, we show how basic policies and obfuscation methods help to keep the genomic privacy of a patient at a high level. We also implement and show, via a complexity analysis, the practicality of PDS.

De-anonymizing Genomic Databases Using Phenotypic Traits

“…First of all, upstream protection of genomic data by cryptographic means would dramatically thwart any attempt to deanonymize this data [4]. It has been proposed to use homomorphic encryption and private set intersection for providing personalized medical tests while preserving privacy of genomic data [5,6]. Although encryption of genomic data does not reduce much utility and efficiency in healthcare, such cryptographic techniques probably add too much overhead in genomic research [21].…”

“…For instance, the relationship between SNPs and phenotypes is increasingly used in forensics for reconstructing facial composites from DNA information [7,8]. 5 Therefore,…”

GenoGuard: Protecting Genomic Data against Brute-Force Attacks

“…The results are shown in Table II. We use the Zipf's model in [30], where N = 486118, W = 0.037871 and s = 0.905773. For different hair colors known by adversary B , we perform the Bernoulli trials with corresponding P ret on the password pool, and estimate Adv(B ) in Equation (8). We repeat the whole experiment 1000 times for each hair color, and the average results are shown in Figure 12.…”

“…Baldi et al [10] propose a set of techniques based on private set operations to address genomic privacy in several important applications, namely, paternity tests, personalized medicine, and genetic compatibility tests. Ayday et al [8] introduce a framework that integrates stream ciphers and order-preserving encryption to store and retrieve raw genomic data in a privacy-preserving manner. Researchers also propose to protect privacy in genomic computation by partitioning the computation through program specialization, according to the sensitivity levels of different parts of the genome data [7].…”

“…Existing mechanisms for protecting the privacy of genomic data include (i) anonymization, which has proven to be ineffective for genomic data [2], [3], (ii) adding noise to published genomic data or statistics for medical research (e.g., to guarantee differential privacy [4], [5], [6]), (iii) computation partitioning [7], and (iv) cryptography (e.g., homomorphic encryption [8], [9], private set intersection [10], etc.). In this work, we focus mainly on the personal use of genomic data, such as healthcare or DTC services.…”