In today's data-driven world, organizations face increasing pressure to comply with data disclosure policies, which require data masking measures and robust access control mechanisms. This paper presents Mascara, a middleware for specifying and enforcing data disclosure policies. Mascara extends traditional access control mechanisms with data masking to support partial disclosure of sensitive data. We introduce data masks to specify disclosure policies flexibly and intuitively and propose a query modification approach to rewrite user queries into disclosure-compliant ones. We present a utility estimation framework to estimate the information loss of masked data based on relative entropy, which Mascara leverages to select the disclosure-compliant query that minimizes information loss. Our experimental evaluation shows that Mascara effectively chooses the best disclosure-compliant query with a success rate exceeding 90%, ensuring users get data with the lowest possible information loss. Additionally, Mascara's overhead compared to normal execution without data protection is negligible, staying lower than 300ms even for extreme scenarios with hundreds of possible disclosure-compliant queries.

In pursuit of real-time data analysis, approximate summarization structures, i.e., synopses, have gained importance over the years. However, existing stream processing systems, such as Flink, Spark, and Storm, do not support synopses as first class citizens, i.e., as pipeline operators. Synopses' implementation is upon users. This is mainly because of the diversity of synopses, which makes a unified implementation difficult. We present Condor, a framework that supports synopses as first class citizens. Condor facilitates the specification and processing of synopsis-based streaming jobs while hiding all internal processing details. Condor's key component is its model that represents synopses as a particular case of windowed aggregate functions. An inherent divide and conquer strategy allows Condor to efficiently distribute the computation, allowing for high-performance and linear scalability. Our evaluation shows that Condor outperforms existing approaches by up to a factor of 75x and that it scales linearly with the number of cores.

Distributed dataflow systems (DDS) are widely employed in graph processing and machine learning (ML), where many of these algorithms are iterative in nature. Typically, DDS achieve fault-tolerance using checkpointing mechanisms or they exploit algorithmic properties to enable fault-tolerance without the need for checkpoints. Recently, for graph processing, we proposed utilizing unblocking checkpointing , to parallelize the execution pipeline and checkpoint writing, as well as confined recovery , to enable fast recovery upon partial node failures. Furthermore, for ML algorithms implemented using broadcast variables, we proposed utilizing replica recovery , to leverage broadcast variable replicas and facilitate failure recovery checkpointing-free. In this demonstration, we showcase these fault-tolerance techniques using Apache Flink. Attendees will be able to: (i) run representative iterative algorithms including PageRank, Connected Components, and K-Means, (ii) explore the internal behavior of DDS under the influence of unblocking checkpointing, and (iii) trigger failures, to observe the effects of confined recovery and replica recovery.