Data privacy in the age of personal genomics

Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy

Motivated by the growing availability of personal genomics services, we study an information-theoretic privacy problem that arises when sharing genomic data: a user wants to share his or her genome sequence while keeping the genotypes at certain positions hidden, which could otherwise reveal critical health-related information. A straightforward solution of erasing (masking) the chosen genotypes does not ensure privacy, because the correlation between nearby positions can leak the masked genotypes. We introduce an erasure-based privacy mechanism with perfect information-theoretic privacy, whereby the released sequence is statistically independent of the sensitive genotypes. Our mechanism can be interpreted as a locally-optimal greedy algorithm for a given processing order of sequence positions, where utility is measured by the number of positions released without erasure. We show that finding an optimal order is NP-hard in general and provide an upper bound on the optimal utility. For sequences from hidden Markov models, a standard modeling approach in genetics, we propose an efficient algorithmic implementation of our mechanism with complexity polynomial in sequence length. Moreover, we illustrate the robustness of the mechanism by bounding the privacy leakage from erroneous prior distributions. Our work is a step towards more rigorous control of privacy in genomic data sharing.

Communication-efficient federated learning via knowledge distillation

Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw data. However, model updates can be extremely large if they contain numerous parameters, and many rounds of communication are needed for model training. The huge communication cost in federated learning leads to heavy overheads on clients and high environmental burdens. Here, we present a federated learning method named FedKD that is both communication-efficient and effective, based on adaptive mutual knowledge distillation and dynamic gradient compression techniques. FedKD is validated on three different scenarios that need privacy protection, showing that it maximally can reduce 94.89% of communication cost and achieve competitive results with centralized model learning. FedKD provides a potential to efficiently deploy privacy-preserving intelligent systems in many scenarios, such as intelligent healthcare and personalization.

This work presents a communication-efficient federated learning method that saves a major fraction of communication cost. It reveals the advantage of reciprocal learning in machine knowledge transfer and the evolutional low-rank properties of deep model updates.

Sociotechnical safeguards for genomic data privacy

Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of genomic data that are collected, used and shared. This state of affairs raises new and challenging concerns for personal privacy, both legally and technically. This Review appraises existing and emerging threats to genomic data privacy and discusses how well current legal frameworks and technical safeguards mitigate these concerns. It concludes with a discussion of remaining and emerging challenges and illustrates possible solutions that can balance protecting privacy and realizing the benefits that result from the sharing of genetic information.

In this Review, the authors describe technical and legal protection mechanisms for mitigating vulnerabilities in genomic data privacy. They also discuss how these protections are dependent on the context of data use such as in research, health care, direct-to-consumer testing or forensic investigations.

Digital DNA lifecycle security and privacy: an overview

DNA sequencing technologies have advanced significantly in the last few years leading to advancements in biomedical research which has improved personalised medicine and the discovery of new treatments for diseases. Sequencing technology advancement has also reduced the cost of DNA sequencing, which has led to the rise of direct-to-consumer (DTC) sequencing, e.g. 23andme.com, ancestry.co.uk, etc. In the meantime, concerns have emerged over privacy and security in collecting, handling, analysing and sharing DNA and genomic data. DNA data are unique and can be used to identify individuals. Moreover, those data provide information on people's current disease status and disposition, e.g. mental health or susceptibility for developing cancer. DNA privacy violation does not only affect the owner but also affects their close consanguinity due to its hereditary nature. This article introduces and defines the term 'digital DNA life cycle' and presents an overview of privacy and security threats and their mitigation techniques for predigital DNA and throughout the digital DNA life cycle. It covers DNA sequencing hardware, software and DNA sequence pipeline in addition to common privacy attacks and their countermeasures when DNA digital data are stored, queried or shared. Likewise, the article examines DTC genomic sequencing privacy and security.

Using game theory to thwart multistage privacy intrusions when sharing data

Person-specific biomedical data are now widely collected, but its sharing raises privacy concerns, specifically about the re-identification of seemingly anonymous records. Formal re-identification risk assessment frameworks can inform decisions about whether and how to share data; current techniques, however, focus on scenarios where the data recipients use only one resource for re-identification purposes. This is a concern because recent attacks show that adversaries can access multiple resources, combining them in a stage-wise manner, to enhance the chance of an attack’s success. In this work, we represent a re-identification game using a two-player Stackelberg game of perfect information, which can be applied to assess risk, and suggest an optimal data sharing strategy based on a privacy-utility tradeoff. We report on experiments with large-scale genomic datasets to show that, using game theoretic models accounting for adversarial capabilities to launch multistage attacks, most data can be effectively shared with low re-identification risk.

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.

Artificial intelligence for proteomics and biomarker discovery

There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings.

Can Blockchain Solve the Dilemma in the Ethics of Genomic Biobanks?

In discussions on the ethics of genome collections and biobanks, the main worry about whether we are permitted to collect and use individuals' genomic and genetic data is the potential for the violation of individuals' right to informational privacy. Yet, if we do not permit these endeavors, we risk giving up on the future benefits of biomedical research. In this paper, I describe a private venture in blockchain genomics that seeks to provide an apt solution to concerns about potential privacy violations in genome collections and biobanks. I then provide some reasons to doubt the kind of solution to the dilemma that such start-ups propose. I argue that the sort of autonomy that grounds the value of the right to informational privacy cannot be secured with blockchain technology alone. So, blockchain genomics ventures are insufficient to establish the permissibility of genome collections and biobanks. I then discuss an additional ethical challenge to genomic biobanks. This challenge takes the form of doubting that future benefits that result from biomedical research dependent on genomic databases will outweigh the costs. Despite criticisms of genomic research, I claim that it is reasonable to think that future net benefits will be gained from research on such databases. Therefore, I conclude that there remains a dilemma in the ethics of genomic biobanks.

Unchaining Collective Intelligence for Science, Research, and Technology Development by Blockchain-Boosted Community Participation

Since its launch just over a decade ago by the cryptocurrency Bitcoin, the distributed ledger technology (DLT) blockchain has followed a breathtaking trajectory into manifold application spaces. This study aper analyses how key factors underpinning the success of this ground-breaking “Internet of value” technology, such as staking of collateral (“skin in the game”), competitive crowdsourcing, crowdfunding, and prediction markets, can be applied to substantially innovate the legacy organization of science, research, and technology development (RTD). Here, we elaborate a highly integrative, community-based strategy where a token-based crypto-economy supports finding best possible consensus, trust, and truth by adding unconventional elements known from reputation systems, betting, secondary markets, and social networking. These tokens support the holder’s formalized reputation and are used in liquid-democracy style governance and arbitration within projects or community-driven initiatives. This participatory research model serves as a solid basis for comprehensively leveraging collective intelligence by effectively incentivizing contributions from the crowd, such as intellectual property work, validation, assessment, infrastructure, education, assessment, governance, publication, and promotion of projects. On the analogy of its current blockbusters like peer-to-peer structured decentralized finance (“DeFi”), blockchain technology can seminally enhance the efficiency of science and RTD initiatives, even permitting to fully stage operations as a chiefless decentralized autonomous organization (DAOs).

Citizen-centered, auditable and privacy-preserving population genomics

The growing number of health-data breaches, the use of genomic databases for law enforcement purposes and the lack of transparency of personal genomics companies are raising unprecedented privacy concerns. To enable a secure exploration of genomic datasets with controlled and transparent data access, we propose a citizen-centric approach that combines cryptographic privacy-preserving technologies, such as homomorphic encryption and secure multi-party computation, with the auditability of blockchains. Our open-source implementation supports queries on the encrypted genomic data of hundreds of thousands of individuals, with minimal overhead. We show that real-world adoption of our system alleviates widespread privacy concerns and encourages data access sharing with researchers.

Complicated legacies: The human genome at 20

Millions of people today have access to their personal genomic information. Direct-to-consumer services and integration with other “big data” increasingly commoditize what was rightly celebrated as a singular achievement in February 2001 when the first draft human genomes were published. But such remarkable technical and scientific progress has not been without its share of missteps and growing pains. Science invited the experts below to help explore how we got here and where we should (or ought not) be going.

A semi-automatic methodology for analysing distributed and private biobanks

Privacy issues limit the analysis and cross-exploration of most distributed and private biobanks, often raised by the multiple dimensionality and sensitivity of the data associated with access restrictions and policies. These characteristics prevent collaboration between entities, constituting a barrier to emergent personalized and public health challenges, namely the discovery of new druggable targets, identification of disease-causing genetic variants, or the study of rare diseases. In this paper, we propose a semi-automatic methodology for the analysis of distributed and private biobanks. The strategies involved in the proposed methodology efficiently enable the creation and execution of unified genomic studies using distributed repositories, without compromising the information present in the datasets. We apply the methodology to a case study in the current Covid-19, ensuring the combination of the diagnostics from multiple entities while maintaining privacy through a completely identical procedure. Moreover, we show that the methodology follows a simple, intuitive, and practical scheme.

Mini Review: Clinical Routine Microbiology in the Era of Automation and Digital Health

Clinical microbiology laboratories are the first line to combat and handle infectious diseases and antibiotic resistance, including newly emerging ones. Although most clinical laboratories still rely on conventional methods, a cascade of technological changes, driven by digital imaging and high-throughput sequencing, will revolutionize the management of clinical diagnostics for direct detection of bacteria and swift antimicrobial susceptibility testing. Importantly, such technological advancements occur in the golden age of machine learning where computers are no longer acting passively in data mining, but once trained, can also help physicians in making decisions for diagnostics and optimal treatment administration. The further potential of physically integrating new technologies in an automation chain, combined to machine-learning-based software for data analyses, is seducing and would indeed lead to a faster management in infectious diseases. However, if, from one side, technological advancement would achieve a better performance than conventional methods, on the other side, this evolution challenges clinicians in terms of data interpretation and impacts the entire hospital personnel organization and management. In this mini review, we discuss such technological achievements offering practical examples of their operability but also their limitations and potential issues that their implementation could rise in clinical microbiology laboratories.

Privacy-Preserving Distributed Analytics in Fog-Enabled IoT Systems

The Internet of Things (IoT) has evolved significantly with advances in gathering data that can be extracted to provide knowledge and facilitate decision-making processes. Currently, IoT data analytics encountered challenges such as growing data volumes collected by IoT devices and fast response requirements for time-sensitive applications in which traditional Cloud-based solution is unable to meet due to bandwidth and high latency limitations. In this paper, we develop a distributed analytics framework for fog-enabled IoT systems aiming to avoid raw data movement and reduce latency. The distributed framework leverages the computational capacities of all the participants such as edge devices and fog nodes and allows them to obtain the global optimal solution locally. To further enhance the privacy of data holders in the system, a privacy-preserving protocol is proposed using cryptographic schemes. Security analysis was conducted and it verified that exact private information about any edge device’s raw data would not be inferred by an honest-but-curious neighbor in the proposed secure protocol. In addition, the accuracy of solution is unaffected in the secure protocol comparing to the proposed distributed algorithm without encryption. We further conducted experiments on three case studies: seismic imaging, diabetes progression prediction, and Enron email classification. On seismic imaging problem, the proposed algorithm can be up to one order of magnitude faster than the benchmarks in reaching the optimal solution. The evaluation results validate the effectiveness of the proposed methodology and demonstrate its potential to be a promising solution for data analytics in fog-enabled IoT systems.

Strategic vision for improving human health at The Forefront of Genomics

Starting with the launch of the Human Genome Project three decades ago, and continuing after its completion in 2003, genomics has progressively come to have a central and catalytic role in basic and translational research. In addition, studies increasingly demonstrate how genomic information can be effectively used in clinical care. In the future, the anticipated advances in technology development, biological insights, and clinical applications (among others) will lead to more widespread integration of genomics into almost all areas of biomedical research, the adoption of genomics into mainstream medical and public-health practices, and an increasing relevance of genomics for everyday life. On behalf of the research community, the National Human Genome Research Institute recently completed a multi-year process of strategic engagement to identify future research priorities and opportunities in human genomics, with an emphasis on health applications. Here we describe the highest-priority elements envisioned for the cutting-edge of human genomics going forward-that is, at 'The Forefront of Genomics'.

Current Status on Population Genome Catalogues in different Countries

Genomics has become indispensable for research in the last two decades. Completed and ongoing genome projects such as the UK Biobank, ICGC, TCGA and GenomeAsia100K have helped to understand several life-threatening diseases like cancer. Such initiatives from different countries have offered genomics-based diagnostics along with glues for therapies towards personalized healthcare. The Indian Agencies has started initiatives to catalogue the genome sequences of 20,000 individuals. The Department of Biotechnology (DBT) along with other scientific agencies has plans to sequence 10,000 healthy individuals and 10,000 diseased individuals. The Council of Scientific and Industrial Research (CSIR) also developed the "IndiGen" genome project where genome sequences for 1008 individuals are made available in Phase I. This will enable the development of a genome catalogue to introduce novel genomics-based clinical applications in future healthcare plan.

Low-contact and high-interconnectivity pathology (LC&HI Path): post-COVID19-pandemic practice of pathology

The COVID-19 pandemic situation may be viewed as an opportunity to accelerate some of the ongoing transformations in modern pathology. This refers primarily to the digitalisation of the practice of tissue and cellular pathology diagnostics. However, it is also an opportunity to analyse the modus operandi of a discipline that has been practised in a similar manner for more than 100 years. The challenge is to define the next generation of interconnectivity tools that would be necessary to achieve a new operational model that, while ensuring low face-to-face interaction between the main players of the diagnostic pipeline, allows maximum interconnectivity to serve our patients and the immediate teaching and research needs associated with clinical tissue/cellular samples. This viewpoint aims to describe what this new paradigm, a low-contact and high-interconnectivity pathology (LC&HC Path) operation, may require in the near future.

Dynamic-informed consent: A potential solution for ethical dilemmas in population sequencing initiatives

While the majority of population-level genome sequencing initiatives claim to follow the principles of informed consent, the requirements for informed consent have not been-well defined in this context. In fact, the implementation of informed consent differs greatly across these initiatives - spanning broad consent, blanket consent, and tiered consent among others. As such, this calls for an investigation into the requirements for consent to be "informed" in the context of population genomics. One particular strategy that claims to be fully informed and to continuously engage participants is called "dynamic consent". Dynamic consent is based on a personalised communication platform that aims to facilitate the consent process. It is oriented to support continuous two-way communication between researchers and participants. In this paper, we analyze the requirements of informed consent in the context of population genomics, review various current implementations of dynamic consent, assess whether they fulfill the requirement of informed consent, and, in turn, enable participants to make autonomous and informed choices on whether or not to participate in research projects.