Side-channel attacks leverage implementation of algorithms to bypass security and leak restricted data. A timing attack observes differences in runtime in response to varying inputs to learn restricted information. Most prior work has focused on applying timing attacks to cryptoanalysis algorithms; other approaches sought to learn about database content by measuring the time of an operation (e.g., index update or query caching). Our goal is to evaluate the practical risks of leveraging a non-privileged user account to learn about data hidden from the user account by access control.

As new laws governing management of personal data are introduced, e.g., the European Union’s General Data Protection Regulation of 2016 and the California Consumer Privacy Act of 2018, compliance with data governance legislation is becoming an increasingly important aspect of data management. An important component of many data privacy laws is that they require companies to only use an individual’s data for a purpose the individual has explicitly consented to. Prior methods for enforcing consent for aggregate queries either use access control to eliminate data without consent from query evaluation or apply differential privacy algorithms to inject synthetic noise into the outcomes of queries (or input data) to ensure that the anonymity of non-consenting individuals is preserved with high probability. Both approaches return query results that differ from the ground truth results corresponding to the full input containing data from both consenting and non-consenting individuals. We present an alternative framework for group-by aggregate queries, tailored for applications, e.g., medicine, where even a small deviation from the correct answer to a query cannot be tolerated. Our approach uses provenance to determine, for each output tuple of a group-by aggregate query, which individual’s data was used to derive the result for this group. We then use statistical tests to determine how likely it is that the presence of data for a non-consenting individual will be revealed by such an output tuple. We filter out tuples for which this test fails, i.e., which are deemed likely to reveal non-consenting data. Thus, our approach always returns a subset of the ground truth query answers. Our experiments successfully return only 100% accurate results in instances where access control or differential privacy would have either returned less total or less accurate results.

Data privacy requirements are a complex and quickly evolving part of the data management domain. Especially in Healthcare (e.g., United States Health Insurance Portability and Accountability Act and Veterans Affairs requirements), there has been a strong emphasis on data privacy and protection. Data storage is governed by multiple sources of policy requirements, including internal policies and legal requirements imposed by external governing organizations. Within a database, a single value can be subject to multiple requirements on how long it must be preserved and when it must be irrecoverably destroyed. This often results in a complex set of overlapping and potentially conflicting policies. Existing storage systems are lacking sufficient support functionality for these critical and evolving rules, making compliance an underdeveloped aspect of data management. As a result, many organizations must implement manual ad-hoc solutions to ensure compliance. As long as organizations depend on manual approaches, there is an increased risk of non-compliance and threat to customer data privacy.

Applications in several areas, such as privacy, security, and integrity validation, require direct access to database management system (DBMS) storage. However, relational DBMSes are designed for physical data independence, and thus limit internal storage exposure. Consequently, applications either cannot be enabled or access storage with ad-hoc solutions, such as querying the ROWID (which can expose physical record location within DBMS storage but not within OS storage) or using DBMS "page repair" tools that read and write DBMS data pages directly. Such ad-hoc methods are limited in their capabilities and difficult to program, maintain, and port across various DBMSes. In this demonstration, we showcase DF-Toolkit - a set of tools that provide an abstracted access to the DBMS storage layer. Users will be able to view DBMS storage not accessible through other applications. Examples include unallocated (e.g., deleted) data, index value-pointer pairs, and cached DBMS pages in RAM. Users will also be able to interact with several special-purpose security applications that audit DBMS storage beyond what DBMS vendors support.

Database Management Systems (DBMSes) secure data against regular users through defensive mechanisms such as access control, and against privileged users with detection mechanisms such as audit logging. Interestingly, these security mechanisms are built into the DBMS and are thus only useful for monitoring or stopping operations that are executed through the DBMS API. Any access that involves directly modifying database files (at file system level) would, by definition, bypass any and all security layers built into the DBMS itself. In this paper, we propose and evaluate an approach that detects direct modifications to database files that have already bypassed the DBMS and its internal security mechanisms. Our approach applies forensic analysis to first validate database indexes and then compares index state with data in the DBMS tables. We show that indexes are much more difficult to modify and can be further fortified with hashing. Our approach supports most relational DBMSes by leveraging index structures that are already built into the system to detect database storage tampering that would currently remain undetectable.

The pervasive use of databases for the storage of critical and sensitive information in many organizations has led to an increase in the rate at which databases are exploited in computer crimes. While there are several techniques and tools available for database forensics, they mostly assume apriori database preparation, such as relying on tamper-detection software to already be in place or use of detailed logging. Alternatively, investigators need forensic tools and techniques that work on poorly-configured databases and make no assumptions about the extent of damage in a database. In this paper, we present our database forensics methods, which are capable of examining database content from a database image without using any log or system metadata. We describe how these methods can be used to detect security breaches in untrusted environments where the security threat arose from a privileged user (or someone who has obtained such privileges).

RDBMSes only support one clustered index per database table that can speed up query processing. Database applications, that continually ingest large amounts of data, perceive slow query response times to long downtimes, as the clustered index ordering must be strictly maintained. In this paper, we show that application slowdown or downtime, however, can often be avoided if database systems expose the physical location of attributes that are completely or approximately clustered.