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Modern data privacy regulations such as GDPR, CCPA, and CDPA stipulate that data pertaining to a user must be deleted without undue delay upon the user’s request. Existing systems are not designed to comply with these regulations and can leave traces of deleted data for indeterminate periods of time, often as long as months. We developed Lethe to address these problems by providing fine-grained secure deletion on any system and any storage medium, provided that Lethe has access to a fixed, small amount of securely-deletable storage. Lethe achieves this using keyed hash forests (KHFs), extensions of keyed hash trees (KHTs), structured to serve as efficient representations of encryption key hierarchies. By using a KHF as a regulator for data access, Lethe provides its secure deletion not by removing the KHF, but by adding a new KHF that only grants access to still-valid data. Access to the previous KHF is lost, and the data it regulated securely deleted, through the secure deletion of the single key that protected the previous KHF. 

Abstract Diatoms are photosynthetic microalgae that fix a significant fraction of the world’s carbon. Because of their photosynthetic efficiency and high-lipid content, diatoms are priority candidates for biofuel production. Here, we report that sporulating Bacillus thuringiensis and other members of the Bacillus cereus group, when in co-culture with the marine diatom Phaeodactylum tricornutum, significantly increase diatom cell count. Bioassay-guided purification of the mother cell lysate of B. thuringiensis led to the identification of two diketopiperazines (DKPs) that stimulate both P. tricornutum growth and increase its lipid content. These findings may be exploited to enhance P. tricornutum growth and microalgae-based biofuel production. As increasing numbers of DKPs are isolated from marine microbes, the work gives potential clues to bacterial-produced growth factors for marine microalgae. 

Persistent Memory (PM) can be used by applications to directly and quickly persist any data structure, without the overhead of a file system. However, writing PM applications that are simultaneously correct and efficient is challenging. As a result, PM applications contain correctness and performance bugs. Prior work on testing PM systems has low bug coverage as it relies primarily on extensive test cases and developer annotations. In this paper we aim to build a system for more thoroughly testing PM applications. We inform our design using a detailed study of 63 bugs from popular PM projects. We identify two application-independent patterns of PM misuse which account for the majority of bugs in our study and can be detected automatically. The remaining application-specific bugs can be detected using compact custom oracles provided by developers. We then present AGAMOTTO, a generic and extensible system for discovering misuse of persistent memory in PM applications. Unlike existing tools that rely on extensive test cases or annotations, AGAMOTTO symbolically executes PM systems to discover bugs. AGAMOTTO introduces a new symbolic memory model that is able to represent whether or not PM state has been made persistent. AGAMOTTO uses a state space exploration algorithm, which drives symbolic execution towards program locations that are susceptible to persistency bugs. AGAMOTTO has so far identified 84 new bugs in 5 different PM applications and frameworks while incurring no false positives.