Autonomy by Bitmark

People want to know if their neighborhoods are safe from COVID-19. They’re willing to share their own information to create this aggregate data, but they need to have assurances that their privacy is protected. We thus need to solve two problems: acquiring and protecting data.

Our solution, Autonomy, is a tool that can forecast local public health in a privacy-respecting manner. It works by merging local, institutional, and governmental data and analyses that data to generate a visualized, numerical score. It can be an early warning system, a danger assessor, and an analytic research tool. 

By empowering individuals to submit their health data with their privacy secured, Autonomy improves the flow of data between the public and the government. It can halt an epidemic in its tracks, with that power growing the more it scales. At higher levels, it can also provide vital data to health institutions.

Halting local spread of a disease is critical in a pandemic. Taiwan demonstrates how this can be successful: despite a population density of 651 inhabitants/km2, it saw only 55 local transmissions amid 443 total cases by June 14th. In contrast, the US, with only 94 inhabitants/km2, has seen two million cases. 

One difference lies in the public’s confidence in their government and its data. Unfortunately, trust in the US government lies at 17%, a historic low: this impacts belief in governmental regulations and data, resulting in activities that run counter to COVID-19 prevention.

Meanwhile, that distrust may be somewhat validated. Governmental COVID-19 data has been vague, in part due to the balkanization of state and federal agencies, but in part because the government has been unable to both meet privacy expectations and tell the public what they want to know. There are ways to properly anonymize health data, ensuring it remains under the control and protection of the individuals, but the government is not up to the task.

The overall problem thus lies in two parts: the need to improve the quality of public-health data and the need to improve the public’s confidence in that information.

Autonomy improves the quality of public-health data by creating neighborhood health forecasts based on aggregation of data input by the users, by those near them, and by verified public sources. The privacy of individual data is ensured through ubiquitous encryption, pooling of data within cohorts, and the creation of a fiduciary relationship with the ultimate data holder.

Functionally, Autonomy works like this: when using Autonomy, a user enters a cohort based on their current location. They will occasionally be asked to contribute information about how they are doing. By analyzing their data in conjunction with those of other cohort members and verified public data, Autonomy develops a fuller picture of a neighborhood’s health status and notifies them when that changes. 

As this scales up, data on more and more neighborhoods becomes available within Autonomy. Spread is mitigated because people in troubled neighborhoods know to stay home, and others know not to visit them. Not only is the quality and surety of infection surveillance dramatically improved through the local contributions, but so are peoples’ faith in that data, because they know it’s drawn from their own experiences and that of their neighbors.

By enabling mass participation in public health, Autonomy serves a variety of different populations.

• It helps citizens to recognize the potential risks in a specific neighborhood. We’ve seen throughout the pandemic that individuals are unhappy in not knowing the precise locations of outbreaks: Autonomy provides that in a privacy preserving way.

• It helps governments to have an overall view of safe zones and hot spots, allowing leaders to make truly data-driven decisions. We’ve seen that most COVID-19 responses have occurred at a large scale: at a minimum in counties in the US, but often states. Leaders want to minimize the impact of mitigation while maximizing its effect; Autonomy supports that.

• It helps institutions to collect and analyze local data, fostering new research in public health. We’ve seen through our work with Pfizer and UC Berkeley that connecting individual data with public health projects is difficult; Autonomy builds on our experience in doing so.

Catalyzed by COVID-19, the goal of the Autonomy project is to protect the health of our citizens and empower them to help each other and the government and other institutions through new forms of communal, data-driven interactions —  both during this pandemic and in the future.

Autonomy was designed to slow and track the spread of COVID-19, meeting the core demands of this challenge. It does so in ways linked to the experiences of our Taiwan-based team, where we’ve seen the success of both top-down approaches delivered by the government and bottom-up approaches used by civil society. Autonomy is our unique development that combines these approaches through its collection of local data and aggregation of national data.

Autonomy is also forward-looking. It creates a new infrastructure for public health and for public engagement, preparing us for the next health situation that requires civic participation.

Bitmark’s expertise is in handling digital rights at scale. We work with partners such as Pfizer and UC Berkeley who excel at public health: they know what data to gather and how to do so. Finally, we’ve also seen Taiwan’s success in weathering this crisis. No one else has this combination. It allows us to create a unique tool for local public-health forecasts.

There are other teams creating apps that involve mass participation, such as The COVID-19 Symptom Study, How We Feel, and Data4Life. But, those approaches aren’t sufficiently local and don’t protect privacy. They cannot show the immediate risk score of a specific location, cannot report other self-reported symptoms, and require users to sign in with either email address or mobile numbers.

Using Autonomy, users can learn the immediate risk score of a specific location (using the aggregated data from authorities and people) and self-report any symptoms to prevent potential outbreak, but we protect users’ privacy by not requiring a personally identifiable login thanks to our well-tested data privacy and security model.

The aggregation of this private and local health data with data from trusted institutions, drawing upon what succeeded in Taiwan, also provides another innovative element: Autonomy can help people and their communities to discover new ways to get better together; and public and private institutions can access neighborhood information in a privacy-preserving way, allowing them to make public-health decisions and to engage in scientific analysis of the data.

The lack of clarity and accountability around an individual’s digital rights has long been a concern when participating in research. Autonomy uses the open-source, public-access Bitmark Protocol to make rights handling transparent. The Protocol works by recording a “rights bundle” on an open, public blockchain. Only the “rights” are recorded in the blockchain - the “data” resides in encrypted data storage. This method gives Autonomy users autonomous access control of personal data.

In addition, our efforts to protect privacy in Autonomy rest on three bulwarks:

1. Data Pooling. Autonomy pools data by grouping citizens into “cohorts” of 450 people who share their data as a group. Thanks to the pooling, no one can access any specific citizen’s data, nor can they identify any specific citizen.

2. Fiduciary Relationships. Each data pool is overseen by a fiduciary, who holds a position of trust and who is legally required to make his best efforts to protect the data. This puts them in a position like a priest or a therapist, where they legally must keep the secrets of their clients.

3. Ubiquitous Encryption. The data that a citizen puts into a pool is encrypted from the second it leaves their device: Autonomy uses end-to-end encryption to protect data in transit and also ensures that the information remains encrypted in its data store. Autonomy is thus like an ambassador, encoding messages before sending them home.

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We believe that daily personal data aggregated at group level can lead to breakthroughs that will help people, their communities, and even the world. 

The immediate outputs are at the local level: people gain more trust in public-health data, while the quality of that data simultaneously improves due to Autonomy’s aggregation of local and national data. This allows people to make better-informed decisions about personal and public health and makes them more likely to do so.

We believe that the improvement of data quality will be a natural result of our collection of local data and its aggregation. The collection of data will come about thanks to the fact that people have felt increasingly confident about data protection in recent years and have become more willing to share if privacy criteria are met. Finally, faith in this data will come about through the fact that trust improves when people reveal their own vulnerabilities, when information is shared broadly, and when relationships are created: all elements found in Autonomy.

Empowering mass participation will create powerful and trustworthy data sets that can provide great insights. In the future, it might detect cancer clusters by recognizing previously unseen socioeconomic factors or assess how common the cold is in local communities. It might help to evaluate the precise effects of pollution or allergens on personal health through the combination of personal, epidemiologic, ecological, and climate data. We’ve seen this sort of empowerment directly through our work in healthcare: we helped UC Berkeley to collect personal health data with DONATE. 

Madelena Ng, a UC Berkeley Fellow and health researcher says, “With an easy-to-use phone app that participants can control, I am hopeful that more women will be comfortable participating in studies and sharing sensitive data like their reproductive health information.” We supported Pfizer in enrolling people in clinical trials while preserving privacy through MATCH. 

Our ultimate goal is to recreate our public-health infrastructure in a secured and autonomous way that is trusted and utilized by the public.

We can reboot public health with Autonomy.

Currently we have 500 people testing a private beta in the US and Taiwan. But, we are rapidly increasing these numbers, as our public health partners, Dr. Ho Mei-Shang and UC Berkeley, believe that COVID-19 is not a 2020 issue: they assert it’s something that will need to be aggressively monitored for the next several years. Thus, in Taiwan,  Dr. Ho, the prestigious epidologist with experiences in controlling SARS, is working with us to reach 100K users by the end of this year and to reach 10% of Taiwan (2.3 million) in 5 years. 

Similarly, UC Berkeley is worried about mitigating the effects of a second wave while still being able to open up, as they’re currently projecting a devastating $200M deficit next year due to COVID-19 fallout. They want to use Autonomy to roll out the UC Berkeley campus relaunch this fall with 43K students. If successful, we would look to launch across all UC and CSU campuses and reach 10% of all combined students and staff (as conservative estimate) in 2021. In 5 years, we plan to reach 10% of all US college students. 

Autonomy gives everyone equal opportunity to contribute health data and equal access to receive the results and  understand the implications for themselves. So our target user is everyone. That’s what public health is all about: getting the entire public involved. This has never happened before, which is one of the biggest public-health problems that we face.

Our goal for Autonomy this year is to help UC Berkeley to relaunch safely and to set a standard for how institutions around the world can relaunch around COVID without endangering public health and without violating citizen privacy. In August, we will run a virtual conference with Blockchain Commons to kickstart global support around these protocols.

For the next five years, our goal is to earn the trust of users so that they will be willing to contribute not just COVID-19 related data, but also other personal health data (medical records, psychological information), to accelerate advancements in public health and to be sure we’re ready for the next pandemic: current trends in climate change, population concentration, urban expansion, and related habitat destruction have put us in much closer contact with each other and with wildlife, which is a potent reservoir for viruses. As scientists and journalists have noted, “Pandemic may become the new norm”. Within five years, we want the app that’s prepared to mitigate that.

Autonomy handles everyone’s personal health data and helps users to control and mobilize it for their own purposes. Our open-source approach to Autonomy and user-exclusive approach to control of data rights is how we have earned the support of our partners. We believe that with support from individuals and institutions, we can evolve this robust solution to fully realize our vision of a next-generation platform for public health.

One of our barriers is time: we need to capture this brief moment when people are actively focused on public health. In our past work, we saw that people cared about their health data but not enough to actively participate in various efforts (at scale): they were more focused on the problems of their everyday life. But COVID-19 has changed this; public health is now on everyone’s mind. We have a special opportunity to capture peoples’ attention and change how they think about their health data data. But we need to do so quickly, before fatigue over the COVID-19 crisis causes people to shift back to other problems. (It’s already happening; we need to act quickly!)

The other barrier is trust. People don’t trust anyone with their data. They give it up because they don’t feel they have a choice, but no one feels good about it. Trust is crucial for mass participation in public health.

We can uniquely deal with the time crunch in large part because we’ve been working on the key technology to enable the safe capture and control of health data for many years, with UC Berkeley and their DONATE program and with Pfizer and their MATCH Program. To further surmount this barrier will ultimately require bringing in other partners, who can provide us with new policy and technological perspectives, and who can multiply our own efforts. We believe we can do so thanks to our focus on open source and transparency.

We also have unique ways to deal with the issue of trust based on the lessons learned from that work with UC Berkeley and Pfizer, and from our experience in Taiwan, which innovated a whole new health system to help people to trust institutions and institutions to trust people. We just need to use those lessons to push past this barrier, starting at UC Berkeley, and then in the rest of the US. We’ve already begun our work here with an education description of our Bulwarks of Privacy. 

In addition, we believe we can increase trust by working with trustworthy organizations such as Dr. Ho and UC Berkeley, who share the same vision and mission, and people we can rely upon.

Full-time staff: 12

Part-time staff: 4

Contractor: 1

Our team members come from Taipei, Silicon Valley, Da Nang, Reykjavik. Our international breadth gives us a unique capacity to attack this international problem, as does our experience with the civic-focused response of Taiwan.

Sean Moss-Pultz (CEO and co-founder)  is recognized as a pioneer of open-source hardware; Moss-Pultz launched and was CEO of Openmoko Inc., the first open-source phone and precursor to iPhone and Android smartphones. 

Casey Alt (Head of Product) is an artist and researcher interested in the transformation of culture through computational technologies. He has held professorships at Duke University and Columbia University.

Christopher Hall (Lead Architect) is an accomplished embedded software programmer of 35 years, Hall has an expertise in Open Source OS and blockchain technology. Hall has also built microcontroller firmware and a range of embedded devices. 

Michael Nguyễn (Head of Operations) scaled his first social networking service (Cyworld) to 4 million users, and also founded and grew his most successful micro-blogging service (Mimo) to 2 million users.

Dr. Ho Mei-Shang is a renowned virologist and epidemiologist with Academia Sinica. Sinica. She was awarded the Contribution Award in SARS Control for her dedication in 2003. During Covid-19, Dr. Ho was one of the first epidemiologists to call upon the severity of this disease and share actionable information on coping with the virus. 

Our other team members’ expertise include Blockchain, Mobile Software, Backend Development, AWS, Kubernetes, International Relations, Gender and Social Studies, and Marketing.

Together, we are all committed to the mission of restoring trust in data.

Bitmark currently has multiple partners for Autonomy’s development and for its pilot community deployment. 

• UC Berkeley School of Public Health, working on pilot app deployment for the local community. 

• Academia Sinica in Taiwan, one of the world’s foremost research institutions, working on epidemic prevention and post-epidemic life consultation. 

• Blockchain Commons, led by internet cryptography pioneer Christopher Allen. Previously, Allen led the efforts behind the TLS and SSL internet security standards.

In the past, we have successfully partnered with many other companies, including KKBOX (to protect music royalty rights), Health2Sync and CTBC Bank (to establish the world’s first citizen-powered diabetes data trust), and HTC (to protect artists’ rights).

Our core business model at launch is twofold. First, we want to provide value to the users of Autonomy itself, helping them to be safer and healthier by understanding the safety level of the neighborhoods that they visit. Second, we want to provide value to the partners who are helping us to roll out our pilots. In the case of UC Berkeley, the value proposition is obvious: they will get to reopen in a safe and responsible way, offsetting the $200M in losses that they are currently predicting.

Internally, these initial deployments will act as a bootstrap. They will serve to prove the efficacy, safety, and privacy of Autonomy. Afterward, we can expand Autonomy by rolling out service plans to new institutions and business organizations that want to stay safe and healthy, while responsibly reopening. Since Autonomy is open-source, we can provide services to launch and support the product such as data storage and new features (similar to the services offered by RedHat for their Linux or by Automattic for Wordpress).

In the short term, we will have investment, user donations, and public grants to fund our work. In the long run, we will be selling services, but also accepting user donations.

Our sustainability model is based on the core ideal that we always want to serve the user first, and we don’t want to require a certain level of disposable income to be able to participate in public health: that’s the point of having autonomy in the first place.

• Join a supportive community of peers, funders, and experts to help advance our work through Solve's nine-month program.
• Receive mentorship and strategic advice from Solve and MIT networks.
• Get media and conference exposure.

Our partnership goal is to activate local launches through local partners. We strive for local community and developer support to enhance our technology, and to add the functionality that individuals and institutions need. Beyond this, Autonomy needs to be financially self-sustaining without sacrificing the users it is meant to support.

We would like to partner with the following groups:

1. Public health institutions or Schools of Public Health: 

Harvard–MIT Health Sciences and Technology and Johns Hopkins Bloomberg School of Public Health, for guidance from their public health, behavioral, and environmental health scientists. 

2. Various labs in MIT: 

Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic) and MIT Institute for Data, Systems, and Society (IDSS), for collaborating with researchers on healthcare, data, and IDSS COVID-19 collaboration (Isolat).

3. Advocacy groups: 

Electronic Frontier Foundation (EFF) and Privacy International (PI), for communicating the results of our solution and pitching our solution at a global level. 

4. Local experts:

For help in launching Autonomy in local districts. We believe that each region’s Autonomy may show different information and ask different questions. We need local experts to tailor Autonomy for those regions and their people. 

5. MIT students:

We would love to partner with MIT students who can work on Autonomy as their final-year project.

• By empowering individuals to submit their health data with their privacy secured, Autonomy improves the flow of data between the public and the government. This elevates the governance of the health information system and governments and institutions will thus have better evidence on making public health related decisions.

• With the winning fund, Autonomy will grow dramatically by extending our new features, from Covid-19 to future health related services. And with the network, including the global fanbase, mentorship and counseling, Autonomy will be able to reach out to a bigger user base and immediately adjust our direction to fit the needs and benefit of humankind.

• Autonomy forecasts local public health, it works by merging local, institutional, and governmental data and analyses that data to generate a visualized, numerical score. The privacy of individual data is ensured through ubiquitous encryption, pooling of data within cohorts, and the creation of a fiduciary relationship with the ultimate data holder. This creates a safe and efficient platform for data collection and we can thus better analyze and aggregate these data for social good.
• By winning the AI for Humanity Prize, Autonomy will be able to improve the accuracy of the numerical score by engaging the data science community. The more precise the score gets, the better it can benefit humanity.