A Scalable Covert Communication Service For Coworkers

Both 5G Internet and COVID-19 pandemic have increasingly prompted thousands of companies and organizations to shift from a centralized office model to a distributed home model, which poses a new requirement: how to securely and rapidly share private data for coworkers on Internet. The covert communication systems are widely used to deliver private information because of the possibility of extending the system to Internet-scale size. However, most existing systems are inadequate to solve the requirement, since either the servers in centralized systems face the risk of being monitored and infiltrated, or the multi-hop routing schemes in decentralized systems lead to diverse attacks and high delivery latency. To this end, we proposed a scalable covert communication service for coworkers, called SC2. For security and hiddenness, we adopt the content slicing and multichannel routing to prevent adversary from monitoring and analyzing data. For scalability, we design a two-hop logic overlay to support low latency routing, and an adaptive channel selction technique to exploit the available bandwidth of the system. To evaluate the performance of SC2, we deploy the system in various IoT clouds and storage clouds. The experimental results demonstrate that SC2 is able to transmit both short messages and bulk content. Under various parameter settings, the delivery latency of SC2 linearly decreases with the number of channels, and SC2 takes full advantage of the available bandwidth with the growing number of users. © 2022 IEEE.

How Attacker Knowledge Affects Privacy Risks: An Analysis Using Probabilistic Programming

Governments and businesses routinely disclose large amounts of private data on individuals, for data analytics. However, despite attempts by data controllers to anonymise data, attackers frequently deanonymise disclosed data by matching it with their prior knowledge. When is a chosen anonymisation method adequate? For this, a data controller must consider attackers befitting their scenario;how does attacker knowledge affect disclosure risk? We present a multi-dimensional conceptual framework for assessing privacy risks given prior knowledge about data. The framework defines three dimensions: distinctness (of input records), informedness (of attacker), and granularity (of anonymisation program output). We model three well-known types of disclosure risk: identity disclosure, attribute disclosure, and quantitative attribute disclosure. We demonstrate how to apply this framework in a health record privacy scenario: We analyse how informing the attacker with COVID-19 infection rates affects privacy risks. We perform this analysis using Privug, a method that uses probabilistic programming to do standard statistical analysis with Bayesian Inference. © 2022 ACM.

Source Inference Attacks in Federated Learning

Federated learning (FL) has emerged as a promising privacy-aware paradigm that allows multiple clients to jointly train a model without sharing their private data. Recently, many studies have shown that FL is vulnerable to membership inference attacks (MIAs) that can distinguish the training members of the given model from the non-members. However, existing MIAs ignore the source of a training member, i.e., the information of the client owning the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients. The leakage of source information can lead to severe privacy issues. For example, identification of the hospital contributing to the training of an FL model for the COVID-19 pandemic can render the owner of a data record from this hospital more prone to discrimination if the hospital is in a high risk region. In this paper, we propose a new inference attack called source inference attack (SIA), which can derive an optimal estimation of the source of a training member. Specifically, we innovatively adopt the Bayesian perspective to demonstrate that an honest-but-curious server can launch an SIA to steal non-trivial source information of the training members without violating the FL protocol. The server leverages the prediction loss of local models on the training members to achieve the attack effectively and non-intrusively. We conduct extensive experiments on one synthetic and five real datasets to evaluate the key factors in an SIA, and the results show the efficacy of the proposed source inference attack. © 2021 IEEE.

Blending data to understand the economic impact of COVID-19.

The data demands during the pandemic heightened the need to blend information from numerous sources to get a more timely and granular picture of economic developments. Ongoing efforts include the Chicago Fed's weekly retail sales estimate, the Census Bureau's work on higher-frequency state-level retail sales data, the Federal Reserve Board's computations of business closures and weekly payrolls, and the academic Opportunity Insights team's estimates of spending, business revenues and employment by income and ZIP code.