Removing hard drives from a data center may expose sensitive data, such as encryption keys or passwords. To prevent exposure, data centers have security policies in place to physically secure drives in the system, and securely delete data from drives that are removed. Despite advances in security technology and best practices, implementation of these security measures is often done incorrectly. We anticipate that physical security will fail, and fixing the issue after the failure is costly and ineffective. We propose Inkpack, a protocol that prevents an attacker from reading data from a drive removed from the data center even if the attacker has the user key linked to the data. An implementation of this protocol encrypts data, and secret splits the key over a number of drives. Recovering the key requires communicating with other drives, thereby denying access to the data if a few drives have been removed. Inkpack also requires the system to verify the validity of individual drives before normal operation. A prototype created within the Ceph storage system executed individual key split, key rebuild, and drive validation operations in 100–150 μs. We also show that our protocol is sensitive to small data write overheads, demonstrating potential performance gains if implemented on smart solid state storage devices, and propose a solution to increase performance.

Droplet microfluidics has become a powerful tool in precision medicine, green biotechnology, and cell therapy for single-cell analysis and selection by virtue of its ability to effectively confine cells. However, there remains a fundamental trade-off between droplet volume and sorting throughput, limiting the advantages of droplet microfluidics to small droplets (<10 pl) that are incompatible with long-term maintenance and growth of most cells. We present a sequentially addressable dielectrophoretic array (SADA) sorter to overcome this problem. The SADA sorter uses an on-chip array of electrodes activated and deactivated in a sequence synchronized to the speed and position of a passing target droplet to deliver an accumulated dielectrophoretic force and gently pull it in the direction of sorting in a high-speed flow. We use it to demonstrate large-droplet sorting with ~20-fold higher throughputs than conventional techniques and apply it to long-term single-cell analysis of Saccharomyces cerevisiae based on their growth rate.

Acquired drug resistance to anticancer targeted therapies remains an unsolved clinical problem. Although many drivers of acquired drug resistance have been identified^1-4, the underlying molecular mechanisms shaping tumour evolution during treatment are incompletely understood. Genomic profiling of patient tumours has implicated apolipoprotein B messenger RNA editing catalytic polypeptide-like (APOBEC) cytidine deaminases in tumour evolution; however, their role during therapy and the development of acquired drug resistance is undefined. Here we report that lung cancer targeted therapies commonly used in the clinic can induce cytidine deaminase APOBEC3A (A3A), leading to sustained mutagenesis in drug-tolerant cancer cells persisting during therapy. Therapy-induced A3A promotes the formation of double-strand DNA breaks, increasing genomic instability in drug-tolerant persisters. Deletion of A3A reduces APOBEC mutations and structural variations in persister cells and delays the development of drug resistance. APOBEC mutational signatures are enriched in tumours from patients with lung cancer who progressed after extended responses to targeted therapies. This study shows that induction of A3A in response to targeted therapies drives evolution of drug-tolerant persister cells, suggesting that suppression of A3A expression or activity may represent a potential therapeutic strategy in the prevention or delay of acquired resistance to lung cancer targeted therapy.