This paper describes a qualitative study that interrogates the types of technology-facilitated coercive control faced by survivors of human trafcking and uncovers potential interventions to aid survivors' recovery.Via semi-structured interviews with 21 participants, including trafcking survivors and professional advocates, we show how trafckers use technology as a lever for control, engaging in surveillance, blackmail, impersonation, and harassment as they compel survivors to stay in the trafcking situation.In recovery, digital footprints keep survivors tethered to their traffcking experience, impacting their digital autonomy, economic mobility, and feelings of safety.Nevertheless, technology can also be a valuable tool for survivors' recovery, connecting them to essential resources and support systems.We discuss the need for interventions and services that account for the specifcity of the trafcking context to help survivors attain digital safety and autonomy, including the potential to adapt existing tech safety services designed for other contexts to human trafcking. CCS Concepts• Human-centered computing → Empirical studies in HCI ; • Security and privacy → Human and societal aspects of security and privacy.

This paper investigates how to incorporate trauma-informed principles into organizational and coordination processesin a high-stakes context: computer security clinics for survivors of intimate partner violence (IPV). IPV survivors often face complex technology-facilitated abuse and computer security clinics aim to assist by connecting survivors with trained experts who help them work toward digital safety. We analyze how clinics' referral processes constitute key sites for trauma-informed, inter-organizational coordination work, especially in light of stakeholders' asymmetrical knowledge of technology. Via semi-structured interviews, we uncover the potential for misalignment between survivors' needs and clinics' services, leading to potentially negative consequences for both survivors and clinic staff. We then design new referral and assessment tools that enable trauma-informed care and improve coordination by functioning as a guide for people seeking services and enabling clinic staff to determine if a survivor is a good fit for the services available. Finally, we show how the new tools improve coordination and outcomes by analyzing 97 referrals received by a clinic over a six month period. We close by discussing the role of structured communication mechanisms in trauma-informed inter-organizational coordination in high-stakes contexts.

Interactive visualization interfaces enable users to efficiently explore, analyze, and make sense of their datasets. However, as data grows in size, it becomes increasingly challenging to build data interfaces that meet the interface designer's desired latency expectations and resource constraints. Cloud DBMSs, while optimized for scalability, often fail to meet latency expectations, necessitating complex, bespoke query execution and optimization techniques for data interfaces. This involves manually navigating a huge optimization space that is sensitive to interface design and resource constraints, such as client vs server data and compute placement, choosing which computations are done offline vs online, and selecting from a large library of visualization-optimized data structures. This paper advocates for a Physical Visualization Design (PVD) tool that decouples interface design from system design to provide design independence. Given an interfaces underlying data flow, interactions with latency expectations, and resource constraints, PVD checks if the interface is feasible and, if so, proposes and instantiates a middleware architecture spanning the client, server, and cloud DBMS that meets the expectations. To this end, this paper presents Jade, the first prototype PVD tool that enables design independence. Jade proposes an intermediate representation called Diffplans to represent the data flows, develops cost estimation models that trade off between latency guarantees and plan feasibility, and implements an optimization framework to search for the middleware architecture that meets the guarantees. We evaluate Jade on six representative data interfaces as compared to Mosaic and Azure SQL database. We find Jade supports a wider range of interfaces, makes better use of available resources, and can meet a wider range of data, latency, and resource conditions.

The mass collection and reuse of social data requires a reimagining of privacy and consent, with particular attention to the (in)equitable distribution of benefits and burdens between researchers and subjects. Instrumenting frontline clinical services to collect and steward data might mitigate the exploitation inherent to data collection---with attention to how subjects can meaningfully participate in stewardship. We explore participatory data stewardship in the context of clinical computer security for survivors of intimate partner violence (IPV). Via semi-structured interviews with IPV support workers, we explore how data are produced within the IPV care ecosystem at the Clinic to End Tech Abuse (CETA). We then conduct design provocations with clients of IPV services and their support workers, exploring possibilities for participatory data mechanisms like open records and dynamic consent. We find participation in data stewardship may benefit clients through improved agency, self-reflection, and control of self-narrative, and that incurred burdens may be alleviated by enlisting trusted stewards. We close with future work for CSCW interrogating how knowledge of digital-safety harms can and should be produced from clinical encounters, towards more equitable ways of knowing.

We demonstrate PVD, a system that visualization designers can use to co-design the interface and system architecture of scalable and expressive visualization.

Result caching is crucial to the performance of data processing systems, but two trends complicate its use. First, immutable datasets make it difficult to efficiently employ powerful result caching techniques like predicate analysis, since predicate analysis typically requires optimized query plans but generating those plans can be costly with data immutability. Second, increased support for user-defined functions (UDFs), which are treated as black boxes by query engines, hinders aggressive result caching. This paper overcomes these problems by introducing 1) a judicious adaptation of predicate analysis on analyzed query plans that avoids unnecessary query optimization, and 2) a UDF translator that transparently compiles UDFs from general purpose languages into native equivalents. We then present Acorn, a concrete implementation of these techniques in Spark SQL that provides speedups of up to 5x across multiple benchmark and real Spark graph processing workloads.