Data retention

Abstract: DRAM cells store data in the form of charge on a capacitor. This charge leaks off over time, eventually causing data to be lost. To prevent this data loss from occurring, DRAM cells must be periodically refreshed. Unfortunately, DRAM refresh operations waste energy and also degrade system performance by interfering with memory requests. These problems are expected to worsen as DRAM density increases.The amount of time that a DRAM cell can safely retain data without being refreshed is called the cell's retention time. In current systems, all DRAM cells are refreshed at the rate required to guarantee the integrity of the cell with the shortest retention time, resulting in unnecessary refreshes for cells with longer retention times. Prior work has proposed to reduce unnecessary refreshes by exploiting differences in retention time among DRAM cells; however, such mechanisms require knowledge of each cell's retention time.In this paper, we present a comprehensive quantitative study of retention behavior in modern DRAMs. Using a temperature-controlled FPGA-based testing platform, we collect retention time information from 248 commodity DDR3 DRAM chips from five major DRAM vendors. We observe two significant phenomena: data pattern dependence, where the retention time of each DRAM cell is significantly affected by the data stored in other DRAM cells, and variable retention time, where the retention time of some DRAM cells changes unpredictably over time. We discuss possible physical explanations for these phenomena, how their magnitude may be affected by DRAM technology scaling, and their ramifications for DRAM retention time profiling mechanisms.

Abstract: Retention errors, caused by charge leakage over time, are the dominant source of flash memory errors. Understanding, characterizing, and reducing retention errors can significantly improve NAND flash memory reliability and endurance. In this paper, we first characterize, with real 2y-nm MLC NAND flash chips, how the threshold voltage distribution of flash memory changes with different retention age — the length of time since a flash cell was programmed. We observe from our characterization results that 1) the optimal read reference voltage of a flash cell, using which the data can be read with the lowest raw bit error rate (RBER), systematically changes with its retention age, and 2) different regions of flash memory can have different retention ages, and hence different optimal read reference voltages. Based on our findings, we propose two new techniques. First, Retention Optimized Reading (ROR) adaptively learns and applies the optimal read reference voltage for each flash memory block online. The key idea of ROR is to periodically learn a tight upper bound, and from there approach the optimal read reference voltage. Our evaluations show that ROR can extend flash memory lifetime by 64% and reduce average error correction latency by 10.1%, with only 768 KB storage overhead in flash memory for a 512 GB flash-based SSD. Second, Retention Failure Recovery (RFR) recovers data with uncorrectable errors offline by identifying and probabilistically correcting flash cells with retention errors. Our evaluation shows that RFR reduces RBER by 50%, which essentially doubles the error correction capability, and thus can effectively recover data from otherwise uncorrectable flash errors.

Abstract: A growing number of online services, such as Google, Yahoo!, and Amazon, are starting to charge users for their storage Customers often use these services to store valuable data such as email, family photos and videos, and disk backups Today, a customer must entirely trust such external services to maintain the integrity of hosted data and return it intact Unfortunately, no service is infallible To make storage services accountable for data loss, we present protocols that allow a thirdparty auditor to periodically verify the data stored by a service and assist in returning the data intact to the customer Most importantly, our protocols are privacy-preserving, in that they never reveal the data contents to the auditor Our solution removes the burden of verification from the customer, alleviates both the customer’s and storage service’s fear of data leakage, and provides a method for independent arbitration of data retention contracts

Abstract: With the rapid deployment of Internet of Things (IoT) technologies and the variety of ways in which IoT-connected sensors collect and use personal data, there is a need for transparency, control, and new tools to ensure that individual privacy requirements are met. To develop these tools, it is important to better understand how people feel about the privacy implications of IoT and the situations in which they prefer to be notified about data collection. We report on a 1,007-participant vignette study focusing on privacy expectations and preferences as they pertain to a set of 380 IoT data collection and use scenarios. Participants were presented with 14 scenarios that varied across eight categorical factors, including the type of data collected (e.g. location, biometrics, temperature), how the data is used (e.g., whether it is shared, and for what purpose), and other attributes such as the data retention period. Our findings show that privacy preferences are diverse and context dependent; participants were more comfortable with data being collected in public settings rather than in private places, and are more likely to consent to data being collected for uses they find beneficial. They are less comfortable with the collection of biometrics (e.g. fingerprints) than environmental data (e.g. room temperature, physical presence). We also find that participants are more likely to want to be notified about data practices that they are uncomfortable with. Finally, our study suggests that after observing individual decisions in just three data-collection scenarios, it is possible to predict their preferences for the remaining scenarios, with our model achieving an average accuracy of up to 86%.