Private Delivery Networks -- Extended Abstract
- URL: http://arxiv.org/abs/2108.07354v1
- Date: Mon, 2 Aug 2021 15:11:48 GMT
- Title: Private Delivery Networks -- Extended Abstract
- Authors: Alex Berke, Nicolas Lee, Patrick Chwalek
- Abstract summary: This work is about alternative e-commerce delivery network models that address rising privacy and wealth inequality concerns.
This includes strategies that mask and add noise to purchase histories, and allow people to "buy privacy" through charitable contributions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The past decade has seen tremendous shifts in how people live, work, and buy
goods, with an increased reliance on e-commerce and deliveries. Purchase
histories generated through e-commerce can be highly personal, revealing
identifying information about individuals and households. Constructing profiles
from these data allows for the targeting of individuals and communities through
practices such as targeted marketing and information campaigns. Furthermore,
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. Events that
accelerated shifts towards e-commerce, such as an infectious disease epidemic,
have also widened equity gaps. This work is about alternative e-commerce
delivery network models that address both rising privacy and wealth inequality
concerns. This includes strategies that mask and add noise to purchase
histories, and allow people to "buy privacy" through charitable contributions.
Related papers
- I Know What You Bought Last Summer: Investigating User Data Leakage in E-Commerce Platforms [1.5488935492091735]
Concerns about the privacy and security of personal information shared on e-commerce platforms have risen.
We examine a selection of popular online e-shops, revealing that nearly 30% of them violate user privacy by disclosing personal information to third parties.
We observe significant data-sharing patterns with platforms like Facebook, which use personal information to build user profiles and link them to social media accounts.
arXiv Detail & Related papers (2025-04-16T09:52:04Z) - Information Discovery in e-Commerce [97.71958017283593]
Information retrieval has a natural role to play in e-commerce, especially in connecting people to goods and services.
The rise in popularity of e-commerce sites has made research on information discovery in e-commerce an increasingly active research area.
Methods for information discovery in e-commerce largely focus on improving the effectiveness of e-commerce search and recommender systems.
arXiv Detail & Related papers (2024-10-08T07:41:01Z) - Differentially Private Data Release on Graphs: Inefficiencies and Unfairness [48.96399034594329]
This paper characterizes the impact of Differential Privacy on bias and unfairness in the context of releasing information about networks.
We consider a network release problem where the network structure is known to all, but the weights on edges must be released privately.
Our work provides theoretical foundations and empirical evidence into the bias and unfairness arising due to privacy in these networked decision problems.
arXiv Detail & Related papers (2024-08-08T08:37:37Z) - Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
We design an intelligent approach to online privacy protection that leverages supervised learning.
By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
arXiv Detail & Related papers (2023-04-06T05:20:16Z) - Building a healthier feed: Private location trace intersection driven
feed recommendations [6.913190961680716]
We propose a consent-first private information sharing paradigm for driving social feeds from users' personal private data.
This work presents a novel technique for designing feeds that represent real offline social connections through private set intersections.
arXiv Detail & Related papers (2022-10-04T21:52:52Z) - Practical Challenges in Differentially-Private Federated Survival
Analysis of Medical Data [57.19441629270029]
In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models.
In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge.
We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
arXiv Detail & Related papers (2022-02-08T10:03:24Z) - Leveraging Online Shopping Behaviors as a Proxy for Personal Lifestyle
Choices: New Insights into Chronic Disease Prevention Literacy [35.340408651740894]
This paper proposes leveraging online shopping behaviors as a proxy for personal lifestyle choices to freshen chronic disease prevention literacy.
Using the lifestyle-related information preceding their first purchases of prescription drugs, we could determine associations between online shoppers' past lifestyle choices and if they suffered from a particular chronic disease.
arXiv Detail & Related papers (2021-04-29T12:05:16Z) - Personal Data Gentrification [5.127089848246933]
We live in an era in which the most valued services are not paid for in money, but in personal data.
We propose Personal Data Enfranchisement as a middle ground, empowering individuals to control the sharing of their personal information.
arXiv Detail & Related papers (2021-03-31T14:26:05Z) - Face to Purchase: Predicting Consumer Choices with Structured Facial and
Behavioral Traits Embedding [53.02059906193556]
We propose to predict consumers' purchases based on their facial features and purchasing histories.
We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers.
Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
arXiv Detail & Related papers (2020-07-14T06:06:41Z) - Give more data, awareness and control to individual citizens, and they
will help COVID-19 containment [74.10257867142049]
Contact-tracing apps are being proposed for large scale adoption by many countries.
A centralized approach raises concerns about citizens' privacy and needlessly strong digital surveillance.
We advocate a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores"
arXiv Detail & Related papers (2020-04-10T20:30:37Z) - The wisdom of the few: Predicting collective success from individual
behavior [0.0]
Small sets of "discoverers" offer reliable success predictions for the brick-and-mortar stores they visit.
We find that the purchasing history alone enables the detection of small sets of discoverers"
Our findings show that companies and organizations with access to large-scale purchasing data can detect the discoverers and leverage their behavior to anticipate market trends.
arXiv Detail & Related papers (2020-01-14T13:52:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.