With the continued rise of scientific computing and the enormous increases in the size of data being processed, scientists must consider whether the processes for transmitting and storing data sufficiently assure the integrity of the scientific data. When integrity is not preserved, computations can fail and result in increased computational cost due to reruns, or worse, results can be corrupted in a manner not apparent to the scientist and produce invalid science results. Technologies such as TCP checksums, encrypted transfers, checksum validation, RAID and erasure coding provide integrity assurances at different levels, but they may not scale to large data sizes and may not cover a workflow from end-to-end, leaving gaps in which data corruption can occur undetected. In this paper we explore an approach of assuring data integrity-considering either malicious or accidental corruption-for workflow executions orchestrated by the Pegasus Workflow Management System. To validate our approach, we introduce Chaos Jungle-a toolkit providing an environment for validating integrity verification mechanisms by allowing researchers to introduce a variety of integrity errors during data transfers and storage. In addition to controlled experiments with Chaos Jungle, we provide analysis of integrity errors that we encountered when running production workflows.

Android and iOS mobile operating systems use permissions to enable phone owners to manage access to their device's resources. Both systems provide resource access dialogues at first use and per-resource controls. Android continues to offer permission manifests in the Android PlayStore for older apps but is transitioning away from this. Neither manifests nor first-use dialogues enable people to easily compare apps based on resource requests, and the corresponding privacy and security risks. Without the ability to compare resource requests when choosing an app, customers cannot select those apps that request fewer resources. Unnecessary and excessive permission requests, overuse of resources, information exfiltration, and risky apps are endemic. To address this issue we built upon past work in warning science and risk communication to design multimedia indicators to communicate the aggregate privacy and security risk associated with an app. Specifically, we provided participants with a privacy rating using the familiar padlock icon and used audio notifications to either warn or reinforce user choices. We empirically tested participants' app decisions with these padlock icons and audio notifications. The results showed that people with both visual cues and audio feedback are more likely to make app choices that are inversely correlated with the resources requested by the app. Those with neither indicators made decisions reflecting only app rating, while decisions made by those with either the audio or the visual indicators are sometimes inversely correlated with resource requests. This illustrates that simple clear communication about apps' aggregate risk, as opposed to atomic resource requests, changes participants' app selections potentially mitigating the state of information overuse and potential abuse. Additionally, neither the visual indicator nor the audio feedback affected the time required for participants to make a decision.