DeepAI AI Chat
Log In Sign Up

Comprehension from Chaos: What Users Understand and Expect from Private Computation

by   Bailey Kacsmar, et al.

Private computation, which includes techniques like multi-party computation and private query execution, holds great promise for enabling organizations to analyze data they and their partners hold while maintaining data subjects' privacy. Despite recent interest in communicating about differential privacy, end users' perspectives on private computation have not previously been studied. To fill this gap, we conducted 22 semi-structured interviews investigating users' understanding of, and expectations for, private computation over data about them. Interviews centered on four concrete data-analysis scenarios (e.g., ad conversion analysis), each with a variant that did not use private computation and one that did (private set intersection, multiparty computation, and privacy preserving query procedures). While participants struggled with abstract definitions of private computation, they found the concrete scenarios enlightening and plausible even though we did not explain the complex cryptographic underpinnings. Private computation increased participants' acceptance of data sharing, but not unconditionally; the purpose of data sharing and analysis was the primary driver of their attitudes. Through co-design activities, participants emphasized the importance of detailing the purpose of a computation and clarifying that inputs to private computation are not shared across organizations when describing private computation to end users.


"I need a better description”: An Investigation Into User Expectations For Differential Privacy

Despite recent widespread deployment of differential privacy, relatively...

Sharing in a Trustless World: Privacy-Preserving Data Analytics with Potentially Cheating Participants

Lack of trust between organisations and privacy concerns about their dat...

DP-PSI: Private and Secure Set Intersection

One way to classify private set intersection (PSI) for secure 2-party co...

Oblivious Sampling Algorithms for Private Data Analysis

We study secure and privacy-preserving data analysis based on queries ex...

Private Product Computation using Quantum Entanglement

In this work, we show that a pair of entangled qubits can be used to com...

Evaluating the End-User Experience of Private Browsing Mode

Nowadays, all major web browsers have a private browsing mode. However, ...