The prevalence of disaggregated storage in public clouds has led to increased latency in modern OLAP cloud databases, particularly when handling ad-hoc and highly-selective queries on large objects. To address this, cloud databases have adopted computation pushdown, executing query predicates closer to the storage layer. However, existing pushdown solutions are inefficient in erasure-coded storage. Cloud storage employs erasure coding that partitions analytics file objects into fixed-sized blocks and distributes them across storage nodes. Consequently, when a specific part of the object is queried, the storage system must reassemble the object across nodes, incurring significant network latency.

The issue of face privacy protection has aroused wide social concern along with the increasing applications of face images. The latest methods focus on achieving a good privacy-utility tradeoff so that the protected results can still be used to support the downstream computer vision tasks. However, they may suffer from limited flexibility in manipulating this tradeoff because the practical requirements may vary under different scenarios. In this paper, we present a novel recurrent latent representation reorganization (LReOrg) framework to deal with the problem. LReOrg relies on two key modules to deal with the privacy-utility tradeoff, where the first one is responsible for anonymizing the privacy sensitive information and the other is responsible for recovering the destroyed useful insensitive information according to user requirements. LReOrg is advantageous in: (a) enabling users to recurrently process fine-grained attributes; (b) providing flexible control over privacy-utility tradeoff by manipulating which attributes to anonymize or preserve using cross-modal keywords; and (c) eliminating the need of data annotations for network training. The experimental results on benchmark datasets have reported the superior ability of our approach for providing flexible protection on facial information.

This study developed an experimental paradigm (CAPTCHA recognition task) with high ecological validity to investigate how continuous errors in an automatic system and the timing of their occurrence affect human-automation trust. The continuous system errors were manipulated to appear at either of the four timing conditions: the early stage, middle stage, late stage of the task, or not showing. Our research found that continuous errors undermines trust in automated systems. More importantly, even with the same average system reliability, overall trust decreases significantly with continuous errors. Human-automation trust is significantly lower in the late continuous error condition compared to the no continuous error condition, indicating that trust in automated systems accords with the peak-end rule. Thus, user trust is mainly affected by the peak and end values of the system reliability. This study provides new suggestions for a trustworthy artificial intelligence design. Although system errors cannot be eliminated thoroughly, developers can minimize their impact on human-automation trust by avoiding continuous errors and preventing them from occurring during the late stage of interaction.

In recent years, emerging storage hardware technologies have focused on divergent goals: better performance or lower cost-per-bit. Correspondingly, data systems that employ these technologies are typically optimized either to be fast (but expensive) or cheap (but slow). We take a different approach: by architecting a storage engine to natively utilize two tiers of fast and low-cost storage technologies, we can achieve a Pareto efficient balance between performance and cost-per-bit.

Modern analytical database systems predominantly rely on column-oriented storage, which offers superior compression efficiency due to the nature of the columnar layout. This compression, however, creates challenges in decoding speed during query processing. Previous research has explored predicate pushdown on encoded values to avoid decoding, but these techniques are restricted to specific encoding schemes and predicates, limiting their practical use. In this paper, we propose a generic predicate pushdown approach that supports arbitrary predicates by leveraging selection pushdown to reduce decoding costs. At the core of our approach is a fast select operator capable of directly extracting selected encoded values without decoding, by using Bit Manipulation Instructions, an instruction set extension to the X86 architecture. We empirically evaluate the proposed techniques in the context of Apache Parquet using both micro-benchmarks and the TPC-H benchmark, and show that our techniques improve the query performance of Parquet by up to one order of magnitude with representative scan queries. Further experimentation using Apache Spark demonstrates speed improvements of up to 5.5X even for end-to-end queries involving complex joins.

Recent years have seen an increased interest towards strong security primitives for encrypted databases (such as oblivious protocols) that hide the access patterns of query execution and reveal only the volume of results. However recent work has shown that even volume leakage can enable the reconstruction of entire columns in the database. Yet existing attacks rely on a set of assumptions that are unrealistic in practice for example they (i) require a large number of queries to be issued by the user or (ii) assume certain distributions on the queries or underlying data (e.g. that the queries are distributed uniformly at random or that the database does not contain missing values). In this work we present new attacks for recovering the content of individual user queries assuming no leakage from the system except the number of results and avoiding the limiting assumptions above. Unlike prior attacks our attacks require only a single query to be issued by the user for recovering the keyword. Furthermore our attacks make no assumptions about the distribution of issued queries or the underlying data. Instead our key insight is to exploit the behavior of real-world applications. We start by surveying 11 applications to identify two key characteristics that can be exploited by attackers-(l) file injection and (ii) automatic query replay. We present attacks that leverage these two properties in concert with volume leakage independent of the details of any encrypted database system. Subsequently we perform an attack on the real Gmail web client by simulating a server-side adversary. Our attack on Gmail completes within a matter of minutes demonstrating the feasibility of our techniques. We also present three ancillary attacks for situations when certain mitigation strategies are employed.

We study how to evaluate Anti-Fingerprinting Privacy Enhancing Technologies (AFPETs). Experimental methods have the advantage of control and precision, and can be applied to new AFPETs that currently lack a user base. Observational methods have the advantage of scale and drawing from the browsers currently in real-world use. We propose a novel combination of these methods, offering the best of both worlds, by applying experimentally created models of a AFPET's behavior to an observational dataset. We apply our evaluation methods to a collection of AFPETs to find the Tor Browser Bundle to be the most effective among them. We further uncover inconsistencies in some AFPETs' behaviors.