Privacy-preserving federated genome-wide association studies via dynamic sampling.

MOTIVATION: Genome-wide association studies (GWAS) benefit from the increasing availability of genomic data and cross-institution collaborations. However, sharing data across institutional boundaries jeopardizes medical data confidentiality and patient privacy. While modern cryptographic techniques provide formal secure guarantees, the substantial communication and computational overheads hinder the practical application of large-scale collaborative GWAS. RESULTS: This work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the accuracy of the results. We propose a novel two-step strategy aimed at reducing communication and computational overheads, and we employ iterative and sampling techniques to ensure accurate results. We instantiate our approach using logistic regression, a commonly used statistical method for identifying associations between genetic markers and the phenotype of interest. We evaluate our proposed methods using two real genomic datasets and demonstrate their robustness in the presence of between-study heterogeneity and skewed phenotype distributions using a variety of experimental settings. The empirical results show the efficiency and applicability of the proposed method and the promise for its application for large-scale collaborative GWAS. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/amioamo/TDS.

Robust Fingerprint of Location Trajectories Under Differential Privacy.

Location-based services have brought significant convenience to people in their daily lives, and the collected location data are also in high demand. However, directly releasing those data raises privacy and liability (e.g., due to unauthorized distribution of such datasets) concerns since location data contain users' sensitive information, e.g., regular moving patterns and favorite spots. To address this, we propose a novel fingerprinting scheme that simultaneously identifies unauthorized redistribution of location datasets and provides differential privacy guarantees for the shared data. Observing data utility degradation due to differentially-private mechanisms, we introduce a utility-focused post-processing scheme to regain spatiotemporal correlations between points in a location trajectory. We further integrate this post-processing scheme into our fingerprinting scheme as a sampling method. The proposed fingerprinting scheme alleviates the degradation in the utility of the shared dataset due to the noise introduced by differentially-private mechanisms (i.e., adds the fingerprint by preserving the publicly known statistics of the data). Meanwhile, it does not violate differential privacy throughout the entire process due to immunity to post-processing, a fundamental property of differential privacy. Our proposed fingerprinting scheme is robust against known and well-studied attacks against a fingerprinting scheme including random flipping attacks, correlation-based flipping attacks, and collusions among multiple parties, which makes it hard for the attackers to infer the fingerprint codes and avoid accusation. Via experiments on two real-life location datasets and two synthetic ones, we show that our scheme achieves high fingerprinting robustness and outperforms existing approaches. Besides, the proposed fingerprinting scheme increases data utility for differentially-private datasets, which is beneficial for data analyzers.

Tuning Privacy-Utility Tradeoff in Genomic Studies Using Selective SNP Hiding.

Researchers need a rich trove of genomic datasets that they can leverage to gain a better understanding of the genetic basis of the human genome and identify associations between phenol-types and specific parts of DNA. However, sharing genomic datasets that include sensitive genetic or medical information of individuals can lead to serious privacy-related consequences if data lands in the wrong hands. Restricting access to genomic datasets is one solution, but this greatly reduces their usefulness for research purposes. To allow sharing of genomic datasets while addressing these privacy concerns, several studies propose privacy-preserving mechanisms for data sharing. Differential privacy is one of such mechanisms that formalize rigorous mathematical foundations to provide privacy guarantees while sharing aggregated statistical information about a dataset. Nevertheless, it has been shown that the original privacy guarantees of DP-based solutions degrade when there are dependent tuples in the dataset, which is a common scenario for genomic datasets (due to the existence of family members). In this work, we introduce a new mechanism to mitigate the vulnerabilities of the inference attacks on differentially private query results from genomic datasets including dependent tuples. We propose a utility-maximizing and privacy-preserving approach for sharing statistics by hiding selective SNPs of the family members as they participate in a genomic dataset. By evaluating our mechanism on a real-world genomic dataset, we empirically demonstrate that our proposed mechanism can achieve up to 40% better privacy than state-of-the-art DP-based solutions, while near-optimally minimizing utility loss.

Privacy preserving identification of population stratification for collaborative genomic research.

The rapid improvements in genomic sequencing technology have led to the proliferation of locally collected genomic datasets. Given the sensitivity of genomic data, it is crucial to conduct collaborative studies while preserving the privacy of the individuals. However, before starting any collaborative research effort, the quality of the data needs to be assessed. One of the essential steps of the quality control process is population stratification: identifying the presence of genetic difference in individuals due to subpopulations. One of the common methods used to group genomes of individuals based on ancestry is principal component analysis (PCA). In this article, we propose a privacy-preserving framework which utilizes PCA to assign individuals to populations across multiple collaborators as part of the population stratification step. In our proposed client-server-based scheme, we initially let the server train a global PCA model on a publicly available genomic dataset which contains individuals from multiple populations. The global PCA model is later used to reduce the dimensionality of the local data by each collaborator (client). After adding noise to achieve local differential privacy (LDP), the collaborators send metadata (in the form of their local PCA outputs) about their research datasets to the server, which then aligns the local PCA results to identify the genetic differences among collaborators' datasets. Our results on real genomic data show that the proposed framework can perform population stratification analysis with high accuracy while preserving the privacy of the research participants.

Privacy-Preserving Database Fingerprinting.

When sharing relational databases with other parties, in addition to providing high quality (utility) database to the recipients, a database owner also aims to have (i) privacy guarantees for the data entries and (ii) liability guarantees (via fingerprinting) in case of unauthorized redistribution. However, (i) and (ii) are orthogonal objectives, because when sharing a database with multiple recipients, privacy via data sanitization requires adding noise once (and sharing the same noisy version with all recipients), whereas liability via unique fingerprint insertion requires adding different noises to each shared copy to distinguish all recipients. Although achieving (i) and (ii) together is possible in a naïve way (e.g., either differentially-private database perturbation or synthesis followed by fingerprinting), this approach results in significant degradation in the utility of shared databases. In this paper, we achieve privacy and liability guarantees simultaneously by proposing a novel entry-level differentially-private (DP) fingerprinting mechanism for relational databases without causing large utility degradation. The proposed mechanism fulfills the privacy and liability requirements by leveraging the randomization nature of fingerprinting and transforming it into provable privacy guarantees. Specifically, we devise a bit-level random response scheme to achieve differential privacy guarantee for arbitrary data entries when sharing the entire database, and then, based on this, we develop an ϵ-entry-level DP fingerprinting mechanism. We theoretically analyze the connections between privacy, fingerprint robustness, and database utility by deriving closed form expressions. We also propose a sparse vector technique-based solution to control the cumulative privacy loss when fingerprinted copies of a database are shared with multiple recipients. We experimentally show that our mechanism achieves strong fingerprint robustness (e.g., the fingerprint cannot be compromised even if the malicious database recipient modifies/distorts more than half of the entries in its received fingerprinted copy), and higher database utility compared to various baseline methods (e.g., application-dependent database utility of the shared database achieved by the proposed mechanism is higher than that of the considered baselines).

Efficient Federated Kinship Relationship Identification.

Kinship relationship estimation plays a significant role in today's genome studies. Since genetic data are mostly stored and protected in different silos, retrieving the desirable kinship relationships across federated data warehouses is a non-trivial problem. The ability to identify and connect related individuals is important for both research and clinical applications. In this work, we propose a new privacy-preserving kinship relationship estimation framework: Incremental Update Kinship Identification (INK). The proposed framework includes three key components that allow us to control the balance between privacy and accuracy (of kinship estimation): an incremental process coupled with the use of auxiliary information and informative scores. Our empirical evaluation shows that INK can achieve higher kinship identification correctness while exposing fewer genetic markers.

Ensuring Trust in Genomics Research.

Reproducibility, transparency, representation, and privacy underpin the trust on genomics research in general and genome-wide association studies (GWAS) in particular. Concerns about these issues can be mitigated by technologies that address privacy protection, quality control, and verifiability of GWAS. However, many of the existing technological solutions have been developed in isolation and may address one aspect of reproducibility, transparency, representation, and privacy of GWAS while unknowingly impacting other aspects. As a consequence, the current patchwork of technological tools only partially and in an overlapping manner address issues with GWAS, sometimes even creating more problems. This paper addresses the progress in a field that creates technological solutions that augment the acceptance and security of population genetic analyses. The text identifies areas that are falling behind in technical implementation or where there is insufficient research. We make the case that a full understanding of the different GWAS settings, technological tools and new research directions can holistically address the requirements for the acceptance of GWAS.

Towards Robust Fingerprinting of Relational Databases by Mitigating Correlation Attacks.

Database fingerprinting is widely adopted to prevent unauthorized data sharing and identify source of data leakages. Although existing schemes are robust against common attacks, their robustness degrades significantly if attackers utilize inherent correlations among database entries. In this paper, we demonstrate the vulnerability of existing schemes by identifying different correlation attacks: column-wise correlation attack, row-wise correlation attack, and their integration. We provide robust fingerprinting against these attacks by developing mitigation techniques, which can work as post-processing steps for any off-the-shelf database fingerprinting schemes and preserve the utility of databases. We investigate the impact of correlation attacks and the performance of mitigation techniques using a real-world database. Our results show (i) high success rates of correlation attacks against existing fingerprinting schemes (e.g., integrated correlation attack can distort 64.8% fingerprint bits by just modifying 14.2% entries in a fingerprinted database), and (ii) high robustness of mitigation techniques (e.g., after mitigation, integrated correlation attack can only distort 3% fingerprint bits). Additionally, the mitigation techniques effectively alleviate correlation attacks even if (i) attackers have access to correlation models directly computed from the original database, while the database owner uses inaccurate correlation models, (ii) or attackers utilizes higher order of correlations than the database owner.

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies.

Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. In this work, we propose a framework that verifies the correctness of the aggregate statistics obtained as a result of a genome-wide association study (GWAS) conducted by a researcher while protecting individuals' privacy in the researcher's dataset. In GWAS, the goal of the researcher is to identify highly associated point mutations (variants) with a given phenotype. The researcher publishes the workflow of the conducted study, its output, and associated metadata. They keep the research dataset private while providing, as part of the metadata, a partial noisy dataset (that achieves local differential privacy). To check the correctness of the workflow output, a verifier makes use of the workflow, its metadata, and results of another GWAS (conducted using publicly available datasets) to distinguish between correct statistics and incorrect ones. For evaluation, we use real genomic data and show that the correctness of the workflow output can be verified with high accuracy even when the aggregate statistics of a small number of variants are provided. We also quantify the privacy leakage due to the provided workflow and its associated metadata and show that the additional privacy risk due to the provided metadata does not increase the existing privacy risk due to sharing of the research results. Thus, our results show that the workflow output (i.e., research results) can be verified with high confidence in a privacy-preserving way. We believe that this work will be a valuable step towards providing provenance in a privacy-preserving way while providing guarantees to the users about the correctness of the results.

Robust fingerprinting of genomic databases.

MOTIVATION: Database fingerprinting has been widely used to discourage unauthorized redistribution of data by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint (distort the steganographic marks, i.e. the embedded fingerprint bit-string) by launching effective correlation attacks, which leverage the intrinsic correlations among genomic data (e.g. Mendel's law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks. RESULTS: Via experiments using a real-world genomic database, we first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g. database accuracy and consistency of SNP-phenotype associations measured via P-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP-phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases, e.g. the mitigation techniques only lead to around 3% loss in accuracy. AVAILABILITY AND IMPLEMENTATION: https://github.com/xiutianxi/robust-genomic-fp-github.

Genomic Data Sharing under Dependent Local Differential Privacy.

Privacy-preserving genomic data sharing is prominent to increase the pace of genomic research, and hence to pave the way towards personalized genomic medicine. In this paper, we introduce (ϵ, T)-dependent local differential privacy (LDP) for privacy-preserving sharing of correlated data and propose a genomic data sharing mechanism under this privacy definition. We first show that the original definition of LDP is not suitable for genomic data sharing, and then we propose a new mechanism to share genomic data. The proposed mechanism considers the correlations in data during data sharing, eliminates statistically unlikely data values beforehand, and adjusts the probability distributions for each shared data point accordingly. By doing so, we show that we can avoid an attacker from inferring the correct values of the shared data points by utilizing the correlations in the data. By adjusting the probability distributions of the shared states of each data point, we also improve the utility of shared data for the data collector. Furthermore, we develop a greedy algorithm that strategically identifies the processing order of the shared data points with the aim of maximizing the utility of the shared data. Our evaluation results on a real-life genomic dataset show the superiority of the proposed mechanism compared to the randomized response mechanism (a widely used technique to achieve LDP).

Facilitating Federated Genomic Data Analysis by Identifying Record Correlations while Ensuring Privacy.

With the reduction of sequencing costs and the pervasiveness of computing devices, genomic data collection is continually growing. However, data collection is highly fragmented and the data is still siloed across different repositories. Analyzing all of this data would be transformative for genomics research. However, the data is sensitive, and therefore cannot be easily centralized. Furthermore, there may be correlations in the data, which if not detected, can impact the analysis. In this paper, we take the first step towards identifying correlated records across multiple data repositories in a privacy-preserving manner. The proposed framework, based on random shuffling, synthetic record generation, and local differential privacy, allows a trade-off of accuracy and computational efficiency. An extensive evaluation on real genomic data from the OpenSNP dataset shows that the proposed solution is efficient and effective.

Genome Reconstruction Attacks Against Genomic Data-Sharing Beacons.

Sharing genome data in a privacy-preserving way stands as a major bottleneck in front of the scientific progress promised by the big data era in genomics. A community-driven protocol named genomic data-sharing beacon protocol has been widely adopted for sharing genomic data. The system aims to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes/no queries on the presence of specific alleles in the dataset. However, beacon protocol was recently shown to be vulnerable against membership inference attacks. In this paper, we show that privacy threats against genomic data sharing beacons are not limited to membership inference. We identify and analyze a novel vulnerability of genomic data-sharing beacons: genome reconstruction. We show that it is possible to successfully reconstruct a substantial part of the genome of a victim when the attacker knows the victim has been added to the beacon in a recent update. In particular, we show how an attacker can use the inherent correlations in the genome and clustering techniques to run such an attack in an efficient and accurate way. We also show that even if multiple individuals are added to the beacon during the same update, it is possible to identify the victim's genome with high confidence using traits that are easily accessible by the attacker (e.g., eye color or hair type). Moreover, we show how a reconstructed genome using a beacon that is not associated with a sensitive phenotype can be used for membership inference attacks to beacons with sensitive phenotypes (e.g., HIV+). The outcome of this work will guide beacon operators on when and how to update the content of the beacon and help them (along with the beacon participants) make informed decisions.

Privacy-preserving and robust watermarking on sequential genome data using belief propagation and local differential privacy.

MOTIVATION: Genome data is a subject of study for both biology and computer science since the start of the Human Genome Project in 1990. Since then, genome sequencing for medical and social purposes becomes more and more available and affordable. Genome data can be shared on public websites or with service providers (SPs). However, this sharing compromises the privacy of donors even under partial sharing conditions. We mainly focus on the liability aspect ensued by the unauthorized sharing of these genome data. One of the techniques to address the liability issues in data sharing is the watermarking mechanism. RESULTS: To detect malicious correspondents and SPs-whose aim is to share genome data without individuals' consent and undetected-, we propose a novel watermarking method on sequential genome data using belief propagation algorithm. In our method, we have two criteria to satisfy. (i) Embedding robust watermarks so that the malicious adversaries cannot temper the watermark by modification and are identified with high probability. (ii) Achieving ϵ-local differential privacy in all data sharings with SPs. For the preservation of system robustness against single SP and collusion attacks, we consider publicly available genomic information like Minor Allele Frequency, Linkage Disequilibrium, Phenotype Information and Familial Information. Our proposed scheme achieves 100% detection rate against the single SP attacks with only 3% watermark length. For the worst case scenario of collusion attacks (50% of SPs are malicious), 80% detection is achieved with 5% watermark length and 90% detection is achieved with 10% watermark length. For all cases, the impact of ϵ on precision remained negligible and high privacy is ensured. AVAILABILITY AND IMPLEMENTATION: https://github.com/acoksuz/PPRW\_SGD\_BPLDP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Curse of Correlations for Robust Fingerprinting of Relational Databases.

Database fingerprinting have been widely adopted to prevent unauthorized sharing of data and identify the source of data leakages. Although existing schemes are robust against common attacks, like random bit flipping and subset attack, their robustness degrades significantly if attackers utilize the inherent correlations among database entries. In this paper, we first demonstrate the vulnerability of existing database fingerprinting schemes by identifying different correlation attacks: column-wise correlation attack, row-wise correlation attack, and the integration of them. To provide robust fingerprinting against the identified correlation attacks, we then develop mitigation techniques, which can work as post-processing steps for any off-the-shelf database fingerprinting schemes. The proposed mitigation techniques also preserve the utility of the fingerprinted database considering different utility metrics. We empirically investigate the impact of the identified correlation attacks and the performance of mitigation techniques using real-world relational databases. Our results show (i) high success rates of the identified correlation attacks against existing fingerprinting schemes (e.g., the integrated correlation attack can distort 64.8% fingerprint bits by just modifying 14.2% entries in a fingerprinted database), and (ii) high robustness of the proposed mitigation techniques (e.g., with the mitigation techniques, the integrated correlation attack can only distort 3% fingerprint bits). Furthermore, we show that the proposed mitigation techniques effectively alleviate correlation attacks even if the attacker has access to the correlation models that are directly calculated from the database.

The effect of kinship in re-identification attacks against genomic data sharing beacons.

MOTIVATION: Big data era in genomics promises a breakthrough in medicine, but sharing data in a private manner limit the pace of field. Widely accepted 'genomic data sharing beacon' protocol provides a standardized and secure interface for querying the genomic datasets. The data are only shared if the desired information (e.g. a certain variant) exists in the dataset. Various studies showed that beacons are vulnerable to re-identification (or membership inference) attacks. As beacons are generally associated with sensitive phenotype information, re-identification creates a significant risk for the participants. Unfortunately, proposed countermeasures against such attacks have failed to be effective, as they do not consider the utility of beacon protocol. RESULTS: In this study, for the first time, we analyze the mitigation effect of the kinship relationships among beacon participants against re-identification attacks. We argue that having multiple family members in a beacon can garble the information for attacks since a substantial number of variants are shared among kin-related people. Using family genomes from HapMap and synthetically generated datasets, we show that having one of the parents of a victim in the beacon causes (i) significant decrease in the power of attacks and (ii) substantial increase in the number of queries needed to confirm an individual's beacon membership. We also show how the protection effect attenuates when more distant relatives, such as grandparents are included alongside the victim. Furthermore, we quantify the utility loss due adding relatives and show that it is smaller compared with flipping based techniques.

Inference attacks against differentially private query results from genomic datasets including dependent tuples.

MOTIVATION: The rapid decrease in the sequencing technology costs leads to a revolution in medical research and clinical care. Today, researchers have access to large genomic datasets to study associations between variants and complex traits. However, availability of such genomic datasets also results in new privacy concerns about personal information of the participants in genomic studies. Differential privacy (DP) is one of the rigorous privacy concepts, which received widespread interest for sharing summary statistics from genomic datasets while protecting the privacy of participants against inference attacks. However, DP has a known drawback as it does not consider the correlation between dataset tuples. Therefore, privacy guarantees of DP-based mechanisms may degrade if the dataset includes dependent tuples, which is a common situation for genomic datasets due to the inherent correlations between genomes of family members. RESULTS: In this article, using two real-life genomic datasets, we show that exploiting the correlation between the dataset participants results in significant information leak from differentially private results of complex queries. We formulate this as an attribute inference attack and show the privacy loss in minor allele frequency (MAF) and chi-square queries. Our results show that using the results of differentially private MAF queries and utilizing the dependency between tuples, an adversary can reveal up to 50% more sensitive information about the genome of a target (compared to original privacy guarantees of standard DP-based mechanisms), while differentially privacy chi-square queries can reveal up to 40% more sensitive information. Furthermore, we show that the adversary can use the inferred genomic data obtained from the attribute inference attack to infer the membership of a target in another genomic dataset (e.g. associated with a sensitive trait). Using a log-likelihood-ratio test, our results also show that the inference power of the adversary can be significantly high in such an attack even using inferred (and hence partially incorrect) genomes. AVAILABILITY AND IMPLEMENTATION: https://github.com/nourmadhoun/Inference-Attacks-Differential-Privacy.

Preserving Genomic Privacy via Selective Sharing.

Although genomic data has significant impact and widespread usage in medical research, it puts individuals' privacy in danger, even if they anonymously or partially share their genomic data. To address this problem, we present a framework that is inspired from differential privacy for sharing individuals' genomic data while preserving their privacy. We assume an individual with some sensitive portion on her genome (e.g., mutations or single nucleotide polymorphisms - SNPs that reveal sensitive information about the individual) that she does not want to share. The goals of the individual are to (i) preserve the privacy of her sensitive data (considering the correlations between the sensitive and non-sensitive part), (ii) preserve the privacy of interdependent data (data that belongs to other individuals that is correlated with her data), and (iii) share as much non-sensitive data as possible to maximize utility of data sharing. As opposed to traditional differential privacy-based data sharing schemes, the proposed scheme does not intentionally add noise to data; it is based on selective sharing of data points. We observe that traditional differential privacy concept does not capture sharing data in such a setting, and hence we first introduce a privacy notation, ϵ-indirect privacy, that addresses data sharing in such settings. We show that the proposed framework does not provide sensitive information to the attacker while it provides a high data sharing utility. We also compare the proposed technique with the previous ones and show our advantage both in terms of privacy and data sharing utility.

Differential privacy under dependent tuples-the case of genomic privacy.

MOTIVATION: The rapid progress in genome sequencing has led to high availability of genomic data. Studying these data can greatly help answer the key questions about disease associations and our evolution. However, due to growing privacy concerns about the sensitive information of participants, accessing key results and data of genomic studies (such as genome-wide association studies) is restricted to only trusted individuals. On the other hand, paving the way to biomedical breakthroughs and discoveries requires granting open access to genomic datasets. Privacy-preserving mechanisms can be a solution for granting wider access to such data while protecting their owners. In particular, there has been growing interest in applying the concept of differential privacy (DP) while sharing summary statistics about genomic data. DP provides a mathematically rigorous approach to prevent the risk of membership inference while sharing statistical information about a dataset. However, DP does not consider the dependence between tuples in the dataset, which may degrade the privacy guarantees offered by the DP. RESULTS: In this work, focusing on genomic datasets, we show this drawback of the DP and we propose techniques to mitigate it. First, using a real-world genomic dataset, we demonstrate the feasibility of an inference attack on differentially private query results by utilizing the correlations between the entries in the dataset. The results show the scale of vulnerability when we have dependent tuples in the dataset. We show that the adversary can infer sensitive genomic data about a user from the differentially private results of a query by exploiting the correlations between the genomes of family members. Second, we propose a mechanism for privacy-preserving sharing of statistics from genomic datasets to attain privacy guarantees while taking into consideration the dependence between tuples. By evaluating our mechanism on different genomic datasets, we empirically demonstrate that our proposed mechanism can achieve up to 50% better privacy than traditional DP-based solutions. AVAILABILITY AND IMPLEMENTATION: https://github.com/nourmadhoun/Differential-privacy-genomic-inference-attack. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

When Biology Gets Personal: Hidden Challenges of Privacy and Ethics in Biological Big Data.

High-throughput technologies for biological data acquisition are advancing at an increasing pace. Most prominently, the decreasing cost of DNA sequencing has led to an exponential growth of sequence information, including individual human genomes. This session of the 2019 Pacific Symposium on Biocomputing presents the distinctive privacy and ethical challenges related to the generation, storage, processing, study, and sharing of individuals' biological data generated by multitude of technologies including but not limited to genomics, proteomics, metagenomics, bioimaging, biosensors, and personal health trackers. The mission is to bring together computational biologists, experimental biologists, computer scientists, ethicists, and policy and lawmakers to share ideas, discuss the challenges related to biological data and privacy.