Repository for Privacy Research done by the Safe Paths community. The repository is maintained by Abhishek and Mikhail. Please make a pull request or reach out to them if you want to get a paper added.
For more privacy and contact tracing research and whitepapers visit Covid Safe Paths Website and Split Learning Research
Adding Location Context to Apple/Google Exposure Notification Bluetooth API: MIT SafePaths Encryption Proposals for GPS + Bluetooth LINK
- Contact tracing requires a strong understanding of location and context of the infection encounters. Although Bluetooth technology does not provide location or context of the encounters, we present key privacy preserving ideas to capture this context that can be used in or alongside the forthcoming Google/Apple Bluetooth Exposure Notification API (GAEN).
SafePaths Privacy Preserving WiFi Co-location LINK
- Estimate whether two devices are close to each other (and the distance between them) while maintaining privacy using WiFi protocols. We want to achieve co-location under two constraints (i) privacy (ii) no crowdsourced pre-recorded SSID/Mac addresses for signal strength or Mac address mapping to GPS locations.
Privacy Guidelines for Contact Tracing Applications LINK
- Contact tracing is a very powerful method to implement and enforce social distancing to avoid spreadingof infectious diseases. The traditional approach of contact tracing is time consuming, manpower intensive, dangerousand prone to error due to fatigue or lack of skill. In this work we discuss the various scenarios which a contact tracing application should be able to handle. Wehighlight the privacy handling of some of the prominent contact tracing applications. Additionally, we describe thevarious threat actors who can disrupt its working, or misuse end user’s data, or hamper its mass adoption. Finally,we present privacy guidelines for contact tracing applications from different stakeholder’s perspective.
Bluetooth based Proximity, Multi-hop Analysis and Bi-directional Trust: Epidemics and More LINK
- In this paper, we propose a trust layer on top of Bluetooth and similar wireless communication technologies that can form mesh networks. This layer as a protocol enables computing trust scores based on proximity and bi-directional transfer of messages in multiple hops across a network of mobile devices. We describe factors and an approach for determining these trust scores and highlight its applications during epidemics such as COVID-19 through improved contact-tracing, better privacy and verification for sensitive data sharing in the numerous Bluetooth and GPS based mobile applications that are being developed to track the spread.
Contact Tracing: Holistic Solution beyond Bluetooth LINK
- Contact tracing is a critical part of reopening society. While manual contact tracing by medical professionals is essential, there’s growing acknowledgement that supplementing this with digital approaches might make a significant difference in the speed with which we can reopen. In this paper, we’ll compare some of the existing technologies that can be used to aid contact tracing and exposure notification efforts, and make the case for using a holistic, multi-modal approach rather than relying exclusively on a single technology.
TRANSPARENCY AND CONSENT - BY DEFAULT LINK
- Difficult times lead to clear perspectives. The coronavirus (COVID-19) pandemic has stopped “life as normal” across much of the world. As governments and healthcare systems put into place strict measures around movement to slow the rate of infection and minimise the death toll, we are coming into a period of significant citizen uncertainty. Managing the outbreak of COVID-19 and flattening the curve is now a global challenge. We read about contact tracing a patient in Korea, Patient 31, a woman who went to the hospital after a car accident, went to a hotel buffet, attended a church, came back to the hospital, and in turn ended up impacting more than a thousand people. The health authorities and contact tracers had to painstakingly interview more than a thousand people to back-trace and see who could be at risk to avoid further spread.
Private GPS Intersection LINK
- Exposure notification in GPS based contact tracing platform requires performing intersection of at least two GPS trails, however, these GPS trails are sensitive information about a user and hence requires performing intersection in a private manner. Low entropy in the GPS trails makes this problem even more challenging as brute force becomes feasible. In this paper, we propose methods and system design for performing private GPS trail intersection using secure cryptographic methods. We also highlight potential attacks which can be performed on the proposed techniques.
Target Privacy Threat Modeling for COVID-19 Exposure Notification Systems LINK
- The adoption of digital contact tracing (DCT) technology during the COVID-19 pandemic has shown multiple benefits, including helping to slow the spread of an infectious disease and to improve the dissemination of accurate information. However, to support both ethical technology deployment and user adoption, privacy must be at the forefront. With the loss of privacy being a critical threat, thorough threat modeling will help us to strategize and protect privacy as digital contact tracing technologies advance. Various threat modeling frameworks exist today, such as LINDDUN, STRIDE, PASTA, and NIST, which focus on software system privacy, system security, application security, and data-centric risk, respectively. When applied to the exposure notification system (ENS) context, these models provide a thorough view of the software side but fall short in addressing the integrated nature of hardware, humans, regulations, and software involved in such systems. Our approach addresses ENSs as a whole and provides a model that addresses the privacy complexities of a multifaceted solution. We define privacy principles, privacy threats, attacker capabilities, and a comprehensive threat model. Finally, we outline threat mitigation strategies that address the various threats defined in our model.
COVID-19 Contact-Tracing Mobile Apps: Evaluation And Assessment For Decision Makers LINK
- More than 150,000 deaths are now attributed to the global COVID-19 pandemic. Many thousands more lives are expected to be lost before we have brought the disease under control and are capable of managing future spikes in the number of cases. In an effort to both slow and stop the disease, communities across the world have halted everyday life, requesting or requiring their residents to close non-essential businesses, stop going to school, and stay home. Digital initiatives hope to support safe and wellconsidered approaches to the reopening of our societies while simultaneously reducing the human loss of life by giving frontline officials modern tools with which to control this pandemic. One particular set of modern digital tools aims to upgrade contact-tracing capacity, typically a lengthy and laborious process. In addition to increasing the speed with which contact-tracers can reach those who have been exposed to the disease, these tools can increase the accuracy of contact tracing. However, many first-generation digital contact-tracing tools have paved the way for a post-pandemic surveillance state and the mistreatment of private, personal information. Privacy must remain at the forefront of the global response, lest short-term pandemic interventions enable long-term surveillance and abuse. The design and development of the next generation of contact-tracing tools offers an opportunity to sharply pivot to solutions using privacy-first principles and collaborative, open-source designs. These tools present an opportunity to save lives by flattening the curve of the pandemic and to provide ecoonomic relief without allowing privacy infringements now or in the future
The Architecture of Trust in Contact Tracing LINK
- If you had to choose between safety and privacy, what would you choose? If you had to choose between your source of income and your individual rights, which would win out? These are no longer hypotheticals; across the world, governments and individuals are being forced to make these decisions.
Contact Tracing to Manage COVID19 Spread – Balancing Personal Privacy and Public Health LINK
- Given promise of digital solutions to mitigate disease spread, it is critical the science of contact tracing be explored, particularly given their cost-efficiency and scalability. It is feasible to manage privacy and public good by innovating appropriate solutions for how data is aggregated and users are informed of exposures. However, potential benefit to address waves of the current pandemic or future outbreaks can’t be under-stated.
Comparing Manual Contact Tracing and Digital Contact Advice LINK
- Manual contact tracing is a top-down solution that starts with contact tracers at the public health level, who identify the contacts of infected individuals, interview them to get additional context about the exposure, and also monitor their symptoms and support them until the incubation period is past. On the other hand, digital contact tracing is a bottom-up solution that starts with citizens who on obtaining a notification about possible exposure to an infected individual may choose to ignore the notification, get tested to determine if they were actually exposed or self-isolate and monitor their symptoms over the next two weeks. Most expertsrecommend a combination of manual contact tracing and digital contact advice but they are not based on a scientific basis. For example, a possible hybrid solution could involve a smartphone based alert that requests the possible contact of an infected individual to call the Public Health (PH) number for next steps, or in some cases, suggest ways to self-assess in order to reduce the burden on PH so only most critical cases require a phone conversation. In this paper, we aim to compare the manual and digital approaches to contact tracing and provide suggestions for potential hybrid solutions.
PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic LINK
- Several contact tracing solutions have been proposed and implemented all around the globe to combat the spread of COVID-19 pandemic. But, most of these solutions endanger the privacy rights of the individuals and hinder their widespread adoption. We propose a privacy-preserving contact tracing protocol for the efficient tracing of the spread of the global pandemic. It is based on the private set intersection (PSI) protocol and utilizes the homomorphic properties to preserve the privacy at the individual level. A hierarchical model for the representation of landscapes and rate-limiting factor on the number of queries have been adopted to maintain the efficiency of the protocol.
Proximity Sensing: Modeling and Understanding Noisy RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracing LINK
- As we await a vaccine, social-distancing via efficient contact tracing has emerged as the primary health strategy to dampen the spread of COVID-19. To enable efficient digital contact tracing, we present a novel system to estimate pair-wise individual proximity, via a joint model of Bluetooth Low Energy (BLE) signals with other on-device sensors (accelerometer, magnetometer, gyroscope). We explore multiple ways of interpreting the sensor data stream (time-series, histogram, etc) and use several statistical and deep learning methods to learn representations for sensing proximity. We report the normalized Decision Cost Function (nDCF) metric and analyze the differential impact of the various input signals, as well as discuss various challenges associated with this task.
DAMS: Meta-Estimation of Private Sketch Data Structures for Differentially Private COVID-19 Contact Tracing LINK
- We propose an improved private count-mean-sketch data structure and show its applicability to differentially private contact tracing. Our proposed scheme (Diversifed Averaging for Meta estimation of Sketches-DAMS) provides a better trade-off between true positive rates and false positive rates while maintaining differential privacy (a widely accepted formal standard for privacy).We show its relevance to the social good application of private digital contact tracing for COVID- 19 and beyond. The scheme involves one way locally differentially private uploads from the infected client devices to a server that upon a post-processing obtains a private aggregated histogram of locations traversed by all the infected clients within a time period of interest. The private aggregated histogram is then downloaded by any querying client in order to compare it with its own data on-device, to determine whether it has come into close proximity of any infected client or not. We present empirical experiments that show a substantial improvement in performance for this particular application. We also prove theoretical variance-reduction guarantees of the estimates obtained through our scheme and verify these findings via experiments as well.
Proximity Inference with Wifi-Colocation during the COVID-19 Pandemic LINK
- In this work we propose using WiFi signals recorded on the phone for performing digital contact tracing. The approach works by scanning the access point information on the device and storing it for future purposes of privacy preserving digital contact tracing. We make our approach resilient to different practical scenarios by configuring a device to turn into hotspot if the access points are unavailable. This makes our proposed approach to be feasible in both dense urban areas as well as sparse rural places. We compare and discuss various shortcomings and advantages of this work over other conventional ways of doing digital contact tracing. Preliminaries results indicate the feasibility and efficacy of our approach for the task of proximity sensing which could be relevant and accurate for its relevance to contact tracing and exposure notifications.
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for Deep Neural Networks LINK
- Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assumption made about data is that it does not carry any sensitive information. This assumption may not hold for many practical cases, especially in the domain where an individual’s personal information is involved, like healthcare and facial recognition systems. We posit that selectively removing features in this latent space can protect the sensitive information and provide better privacy-utility trade-off. Consequently, we propose DISCO which learns a dynamic and data driven pruning filter to selectively obfuscate sensitive information in the feature space. We propose diverse attack schemes for sensitive inputs & attributes and demonstrate the effectiveness of DISCO against state-ofthe- art methods through quantitative and qualitative evaluation. Finally, we also release an evaluation benchmark dataset of 1 million sensitive representations to encourage rigorous exploration of novel attack schemes.
Challenges of Equitable Vaccine Distribution in the COVID-19 Pandemic LINK
- As several COVID-19 vaccine candidates approach approval for human use, governments around the world are preparing comprehensive standards for vaccine distribution and monitoring to avoid long-term consequences that may result from rush-to-market. In this early draft article, we identify challenges for vaccine distribution in four core areas - logistics, health outcomes, user-centric impact, and communication. Each of these challenges is analyzed against five critical consequences impacting disease-spread, individual behaviour, society, the economy, and data privacy. Disparities in equitable distribution, vaccine efficacy, duration of immunity, multi-dose adherence, and privacy-focused record keeping are among the most critical diculties that must be addressed. While many of these challenges have been previously identied and planned for, some have not been acknowledged from a comprehensive view to account for unprecedented repercussions in specific subsets of the population. The logistics of equitable, widespread vaccine distribution in disparate populations and countries of various economic, racial, and cultural constitutions requires careful planning and consideration for global vaccine success. We also describe unique challenges regarding vaccine efficacy in specialized populations including children, the elderly, and immunocompromised individuals Furthermore, we report the potential for understudied drug-vaccine interactions as well as the possibility that certain vaccine platforms may increase susceptibility to HIV infection. Given these complicated issues, the importance of privacy-focused, user-centric systems for vaccine education and incentivization along with clear communication from governments, organizations, and academic institutions is imperative. These challenges are by no means insurmountable, but require thorough consideration to avoid consequences that span a range of disease-related, individual, societal, economic, and security domains.
Clinical Landscape of COVID-19 Testing: Difficult Choices LINK
- The coronavirus disease 2019 (COVID-19) pandemic has spread rapidly across the world, leading to enormous amounts of human death and economic loss. Until definitive preventive or curative measures are developed, policies regarding testing, contact tracing, and quarantine remain the best public health tools for curbing viral spread. Testing is a crucial component of these efforts, enabling the identification and isolation of infected individuals. Differences in testing methodologies, time frames, and outcomes can have an impact on their overall efficiency, usability and efficacy. In this early draft, we draw a comparison between the various types of diagnostic tests including PCR, antigen, and home tests in relation to their relative advantages, disadvantages, and use cases. We also look into alternative and unconventional methods. Further, we analyze the short-term and long-term impacts of the virus and its testing on various verticals such as business, government laws, policies, and healthcare.
Digital Landscape of COVID-19 Testing: Challenges and Opportunities LINK
- The COVID-19 Pandemic has left a devastating trail all over the world, in terms of loss of lives, economic decline, travel restrictions, trade deficit, and collapsing economy including real-estate, job loss, loss of health benefits, the decline in quality of access to care and services and overall quality of life. Immunization from the anticipated vaccines will not be the stand-alone guideline that will help surpass the pandemic and return to normalcy. Four pillars of effective public health intervention include diagnostic testing for both asymptomatic and symptomatic individuals, contact tracing, quarantine of individuals with symptoms or who are exposed to COVID-19, and maintaining strict hygiene standards at the individual and community level. Digital technology, currently being used for COVID-19 testing include certain mobile apps, web dashboards, and online self-assessment tools. Herein, we look into various digital solutions adapted by communities across universities, businesses, and other organizations. We summarize the challenges experienced using these tools in terms of quality of information, privacy, and user-centric issues. Despite numerous digital solutions available and being developed, many vary in terms of information being shared in terms of both quality and quantity, which can be overwhelming to the users. Understanding the testing landscape through a digital lens will give a clear insight into the multiple challenges that we face including data privacy, cost, and miscommunication. It is the destiny of digitalization to navigate testing for COVID-19. Block-chain based systems can be used for privacy preservation and ensuring ownership of the data to remain with the user. Another solution involves having digital health passports with relevant and correct information. In this early draft, we summarize the challenges and propose possible solutions to address the same.
COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms LINK
- The COVID-19 pandemic has challenged scientists and policy-makers internationally to develop novel approaches to public health policy. Furthermore, it has also been observed that the prevalence and spread of COVID-19 varies across different spatial, temporal and demographics. Despite ramping up testing, we still are not at the required level in most parts of the globe. Therefore, we utilize self-reported symptoms survey data to understand trends in the spread of COVID-19. The aim of this study is to segment populations that are highly susceptible. In order to understand such populations, we perform exploratory data analysis, outbreak prediction, and time-series forecasting using public health and policy datasets. From our studies, we try to predict the likely % of population that tested positive for COVID-19 based on selfreported symptoms. Our findings reaffirm the predictive value of symptoms, such as anosmia and ageusia. And we forecast that the % of population having COVID-19-like illness (CLI) and those tested positive as 0.15% and 1.14% absolute error respectively. These findings could help aid faster development of the public health policy, particularly in areas with low levels of testing and having a greater reliance on selfreported symptoms. Our analysis sheds light on identifying clinical attributes of interest across different demographics. We also provide insights into the effects of various policy enactments on COVID-19 prevalence.
Verifiable Proof of Health using Public Key Cryptography LINK
- In the current pandemic, testing continues to be the most important tool for monitoring and curbing the disease spread and early identification of the disease to perform health-related interventions like quarantine, contact tracing and etc. Therefore, the ability to verify the testing status is pertinent as public places prepare to safely open. Recent advances in cryptographic tools have made it possible to build a secure and resilient digital-id system. In this work, we propose to build an end to end COVID-19 results verification protocol that takes privacy, computation, and other practical concerns into account for designing an inter-operable layer of testing results verification system that could potentially enable less stringent and more selective lockdowns. We also discuss various concern encompassing the security, privacy, ethics and equity aspect of the proposed system.
MIT SAFEPATHS CARD (MISACA): AUGMENTING PAPER BASED VACCINATION CARDS WITH PRINTED CODES LINK
- In this early draft, we describe a user-centric, card-based system for vaccine distribution. Our system makes use of digitally signed QR codes and their use for phased vaccine distribution, vaccine administration/record-keeping, immunization verification, and follow-up symptom reporting. Furthermore, we propose and describe a complementary scanner app system to be used by vaccination clinics, public health officials, and immunization verification parties to effectively utilize card-based framework. We believe that the proposed system provides a privacypreserving and efficient framework for vaccine distribution in both developed and developing regions.
Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geo-spatial Technologies LINK
- There is a growing need for spatial privacy considerations in the many geo-spatial technologies that have been created as solutions for COVID-19-related issues. Although effective geo-spatial technologies have already been rolled out, most have significantly sacrificed privacy for utility. In this paper, we explore spatial kanonymity, a privacy-preserving method that can address this unnecessary tradeoff by providing the best of both privacy and utility. After evaluating its past implications in geo-spatial use cases, we propose applications of spatial k-anonymity in the data sharing and managing of COVID-19 contact tracing technologies as well as heat maps showing a user’s travel history. We then justify our propositions by comparing spatial k-anonymity with several other spatial privacy methods, including differential privacy, geo-indistinguishability, and manual consent based redaction. Our hope is to raise awareness of the ever-growing risks associated with spatial privacy and how they can be solved with Spatial K-anonymity.
COVID-19 Tests Gone Rogue: Privacy, Efficacy, Mismanagement and Misunderstandings LINK
- COVID-19 testing, the cornerstone for effective screening and identification of COVID-19 cases, remains paramount as an intervention tool to curb the spread of COVID-19 both at local and national levels. However, the speed at which the pandemic struck and the response was rolled out, the widespread impact on healthcare infrastructure, the lack of sufficient preparation within the public health system, and the complexity of the crisis led to utter confusion among test takers. Invasion of privacy remains a crucial concern. The user experience of test takers remains low. User friction affects the user behavior and discourages participation in testing programs. Test efficacy has been overstated. Test results are poorly understood resulting in inappropriate follow-up recommendations. Herein, we review the current landscape of COVID-19 testing, identify four key challenges, and discuss the consequences of the failure to address these challenges. The current infrastructure around testing and information propagation is highly privacy invasive and does not leverage scalable digital components. In this work, we discuss challenges complicating the existing covid-19 testing ecosystem and highlight the need to improve the testing experience for the user and reduce privacy invasions. Digital tools will play a critical role in resolving these challenges.