COPYRIGHT © 2023 Pokhriyal and Koebe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. AI-assisted diplomatic decision-making during crises—Challenges and opportunities

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AI-assisted diplomatic decision-making during crises—Challenges and opportunities

The increasing prevalence of data breaches necessitates robust data protection measures in computational tasks. Secure computation outsourcing (SCO) presents a viable solution by safeguarding the confidentiality of inputs and outputs in data processing without disclosure. Nonetheless, this approach assumes the existence of a trustworthy coordinator to orchestrate and oversee the process, typically implying that data owners must fulfill this role themselves. In this paper, we consider secure delegated data processing (SDDP), an expanded data processing scenario wherein data owners simply delegate their data to SDDP providers for subsequent value mining or other downstream applications, eliminating the necessary involvement of data owners or trusted entities to dive into data processing deeply. However, general-purpose SDDP poses significant challenges in permitting the discretionary execution of computational tasks by SDDP providers on sensitive data while ensuring confidentiality. Existing approaches are insufficient to support SDDP in either efficiency or universality. To tackle this issue, we propose TGCB, a TEE-based General-purpose Computational Backend, designed to endow general-purpose computation with SDDP capabilities from an engineering perspective, powered by TEE-based code integrity and data confidentiality. Central to TGCB is the Encryption Programming Language (EPL) that defines computational tasks in SDDP. Specifically, SDDP providers can express arbitrary computable functions as EPL scripts, processed by TGCB's interfaces, securely interpreted and executed in TEE, ensuring data confidentiality throughout the process. As a universal computational backend, TGCB extensively bolsters data security in existing general-purpose computational tasks, allowing data owners to leverage SDDP without privacy concerns.

TEE-based General-purpose Computational Backend for Secure Delegated Data Processing

Systemic property dispossession from minority groups has often been carried out in the name of technological progress. In this paper, we identify evidence that the current paradigm of large language models (LLMs) likely continues this long history. Examining common LLM training datasets, we find that a disproportionate amount of content authored by Jewish Americans is used for training without their consent. The degree of over-representation ranges from around 2x to around 6.5x. Given that LLMs may substitute for the paid labor of those who produced their training data, they have the potential to cause even more substantial and disproportionate economic harm to Jewish Americans in the coming years. This paper focuses on Jewish Americans as a case study, but it is probable that other minority communities (e.g., Asian Americans, Hindu Americans) may be similarly affected and, most importantly, the results should likely be interpreted as a"canary in the coal mine"that highlights deep structural concerns about the current LLM paradigm whose harms could soon affect nearly everyone. We discuss the implications of these results for the policymakers thinking about how to regulate LLMs as well as for those in the AI field who are working to advance LLMs. Our findings stress the importance of working together towards alternative LLM paradigms that avoid both disparate impacts and widespread societal harms.

A Canary in the AI Coal Mine: American Jews May Be Disproportionately Harmed by Intellectual Property Dispossession in Large Language Model Training

There is a growing need for responsible spatiotemporal data sharing in our daily lives. Applications such as connected vehicles and mobile advertising are currently undergoing a significant digital shift, demanding new standards for privacy-aware solutions and the integration of machine learning technologies. In this tutorial, we present the concepts and challenges encountered when maximizing the utility of spatiotemporal data while enforcing rigorous privacy and security measures. We review modern data sharing mechanisms that provide stakeholders with the power to establish precise terms for the usage and sharing of their data, secured by a robust data infrastructure. We will explore how such sharing mechanisms interplay with complex privacy stipulations and advanced spatiotemporal analytics. Attendees will leave with a comprehensive understanding of how to navigate the delicate balance of spatiotemporal data usage, paving the way for innovation in privacy and compliance methodologies across various industries.

Responsible Sharing of Spatiotemporal Data

Organizations that would mutually benefit from pooling their data are otherwise wary of sharing. This is because sharing data is costly-in time and effort-and, at the same time, the benefits of sharing are not clear. Without a clear cost-benefit analysis, participants default in not sharing. As a consequence, many opportunities to create valuable data-sharing consortia never materialize, and the value of data remains locked. We introduce a new sharing model, market protocol, and algorithms to incentivize the creation of data-sharing markets. The combined contributions of this paper, which we call DSC, incentivize the creation of data-sharing markets that unleash the value of data for its participants. The sharing model introduces two incentives; one that guarantees that participating is better than not doing so and another that compensates participants according to how valuable their data is. Because operating the consortia is costly, we are also concerned with ensuring its operation is sustainable: we design a protocol that ensures that a valuable data-sharing consortium forms when it is sustainable. We introduce algorithms to elicit the value of data from the participants, which is used first to cover the costs of operating the consortia and second to compensate for data contributions. For the latter, we challenge using the Shapley value to allocate revenue. We offer analytical and empirical evidence for this and introduce an alternative method that compensates participants better and leads to the formation of data-sharing consortia.

Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia

Pooling and sharing data increases and distributes its value. But since data cannot be revoked once shared, scenarios that require controlled release of data for regulatory, privacy, and legal reasons default to not sharing. Because selectively controlling what data to release is difficult, the few data-sharing consortia that exist are often built around data-sharing agreements resulting from long and tedious one-off negotiations. We introduce Data Station, a data escrow designed to enable the formation of data-sharing consortia. Data owners share data with the escrow knowing it will not be released without their consent. Data users delegate their computation to the escrow. The data escrow relies on delegated computation to execute queries without releasing the data first. Data Station leverages hardware enclaves to generate trust among participants, and exploits the centralization of data and computation to generate an audit log. We evaluate Data Station on machine learning and data-sharing applications while running on an untrusted intermediary. In addition to important qualitative advantages, we show that Data Station: i) outperforms federated learning baselines in accuracy and runtime for the machine learning application; ii) is orders of magnitude faster than alternative secure data-sharing frameworks; and iii) introduces small overhead on the critical path.

pdf/data-station-delegated-trustworthy-and-auditable-computation-2q2yoe9p.pdf

Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow

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Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow