The following is a thought piece modeled after a scientific publication from the year 2030. The technologies described here are extrapolations of existing methods based upon current sociotechnical trends, specifically those that may reshape the balance between rural, urban, and nomadic populations. Both real and fictional sources are cited throughout the piece. This publication is a part of Media Lab X.0: Anthology of Tomorrows.
Authors Alex Berke (City Science), Nicolas Lee (Mediated Matter), Patrick Chwalek (Responsive Environments), and Jake Read (Center for Bits and Atoms)
Emerging Trends and Innovations: Nomadic Systems Group 2030 Report
The past years of climate crises and health epidemics have led to the proliferation of nomadic communities and a near reliance on e-commerce and personal deliveries. This has resulted in growing privacy concerns surrounding the personal data connected to such digital and physical transactions. These changes have also widened equity gaps, in terms of both wealth and privacy equity, as lower-income demographics have faced disproportionate challenges in the rapid economic shifts. These challenges include the low availability of affordable privacy-enhancing services, which makes specific groups of people vulnerable for exploitation.
Purchase histories are highly personal and can reveal identifying information about individuals and households. Constructing profiles from this data allows for the targeting of individuals and communities through practices such as individually tailored advertisements or focused information campaigns. When purchase profiles are connected with delivery addresses, these data can measure the demographics of a local community and allow for individualized targeting to reach beyond the digital realm to the physical one. This information has been highly valuable for companies to advance their market dominance and for government agencies to improve their population statistics with a more complete and real-time data source.
This work surveys recent innovations and systems that address the rising privacy and wealth inequality concerns in e-commerce and delivery networks. We describe the privacy threats of e-commerce and deliveries, and analyze how recent innovations have approached these issues by aligning personal incentives with market forces. This includes hacks on the Amazon Locker system, systems to mask and add noise to personal purchase histories, and the use of Private Mutual Aid Networks, which allow higher-income individuals to buy privacy while contributing to those in need.
Privacy; distributed networks; inequality; public-key cryptography; autonomous vehicles
In the past decade, global populations have experienced tremendous shifts in how people live, work, and buy goods. The number of US citizens living far from urban centers has grown dramatically, with rural populations outnumbering urban centers for the first time in over a century. Some of this migration can be explained by loss of habitable space due to rising sea levels, increasingly destructive weather events, and extreme heat. Health crises such as the 2020 COVID-19 pandemic and subsequent infectious disease outbreaks disproportionately impacted urban areas as well, causing migratory surges in which residents rapidly relocated to rural settings. While the first half of the past decade saw a modest migration from cities and trending nomadic communities, the health and climate crises in more recent years have impacted new demographics, going beyond those who could afford to migrate, to those forced to migrate.
The rapid nature of this shift towards nomadic living and informal settlements likely contributed to the new reliance on e-commerce and deliveries . These economic and societal shifts have also been coupled with increased mistrust of third-party data collection (Figure 1), and have led to a novel set of strategies to disrupt data collection. These strategies were initially pioneered by certain communities who were able to leverage their nomadic lifestyles and high levels of community trust and cooperation in order to prototype simple systems for community use. By deliberately obscuring purchase history and geolocation from corporate and federal entities, individuals in these communities could dissociate previously constructed data profiles from their residence and activities. Their early strategies have since evolved into a variety of systems that span communities and geographies, many of which have become commercialized services. This in turn has driven more aggressive methods of data harvesting by vendors.
Online vendors have great insight into people’s demographics and personal identities based on their purchase histories. For example, they can infer the size of a household based on the frequency and size of their orders of simple necessities like toilet paper, or infer whether someone is single, pregnant, or has children of a certain age, based on what they buy. They might infer income level by the quality of items purchased, education level or profession based on books and professional supplies, ethnicity based on food items, race based on hair products, whether someone is male or female based on personal hygiene products, and more, with all of the personal nuances in between. Purchase histories have become especially informative with their recent links to individuals’ medications data  and credit profiles . Furthermore, when purchases are tied to physical delivery addresses, this makes available to companies census geographical information about their customers.
Detailed customer data is highly valuable to companies who use it to win more market share, more data collection channels, and then more leverage against rivals. The consolidation of smaller and mid-sized vendors into larger conglomerates has further limited the options that remote and nomadic communities have to order essential products. Individuals with economic means may access the services that handle their purchase and location privacy, but methods that allow for data protection regardless of socio-economic status are scarce. As a result, the individuals most susceptible to the risks of targeted data collection and privacy loss, such as the risks of predatory marketing and targeted disinformation campaigns, are those with the most to lose from such practices. This report highlights the advances in private delivery network systems that have benefited the populations most in need.