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Sunoo Park

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AAAI Conference 2025 Conference Paper

The Pitfalls of “Security by Obscurity” and What They Mean for Transparent AI

Peter Hall
Olivia Mundahl
Sunoo Park

Calls for transparency in AI systems are growing in number and urgency from diverse stakeholders ranging from regulators to researchers to users (with a comparative absence of companies developing AI). Notions of transparency for AI abound, each addressing distinct interests and concerns. In computer security, transparency is likewise regarded as a key concept. The security community has for decades pushed back against so-called security by obscurity - the idea that hiding how a system works protects it from attack - against significant pressure from industry and other stakeholders, e.g., (Bellovin and Bush 2002). And over those decades, in a community process that is imperfect and ongoing, security researchers and practitioners have gradually built up some norms and practices around how to balance transparency interests with possible negative side effects. This paper asks: What insights can the AI community take from the security community's experience with transparency? We identify three key themes in the security community's perspective on the \emph{benefits of transparency} and their approach to balancing transparency against countervailing interests. For each, we investigate parallels and insights relevant to transparency in AI. We then provide a case study discussion on how transparency has shaped the research subfield of anonymization. Finally, shifting our focus from similarities to differences, we highlight key transparency issues where modern AI systems present challenges different from other kinds of security-critical systems, raising interesting open questions for the security and AI communities alike.

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STOC Conference 2020 Conference Paper

Data structures meet cryptography: 3SUM with preprocessing

Alexander Golovnev
Siyao Guo 0001
Thibaut Horel
Sunoo Park
Vinod Vaikuntanathan

This paper shows several connections between data structure problems and cryptography against preprocessing attacks. Our results span data structure upper bounds, cryptographic applications, and data structure lower bounds, as summarized next. First, we apply Fiat-Naor inversion, a technique with cryptographic origins, to obtain a data structure upper bound. In particular, our technique yields a suite of algorithms with space S and (online) time T for a preprocessing version of the N -input 3SUM problem where S 3 · T = O ( N 6 ). This disproves a strong conjecture (Goldstein et al., WADS 2017) that there is no data structure that solves this problem for S = N 2−δ and T = N 1−δ for any constant δ>0. Secondly, we show equivalence between lower bounds for a broad class of (static) data structure problems and one-way functions in the random oracle model that resist a very strong form of preprocessing attack. Concretely, given a random function F : [ N ] → [ N ] (accessed as an oracle) we show how to compile it into a function G F : [ N 2 ] → [ N 2 ] which resists S -bit preprocessing attacks that run in query time T where ST = O ( N 2−ε ) (assuming a corresponding data structure lower bound on 3SUM). In contrast, a classical result of Hellman tells us that F itself can be more easily inverted, say with N 2/3 -bit preprocessing in N 2/3 time. We also show that much stronger lower bounds follow from the hardness of kSUM. Our results can be equivalently interpreted as security against adversaries that are very non-uniform, or have large auxiliary input, or as security in the face of a powerfully backdoored random oracle. Thirdly, we give non-adaptive lower bounds for 3SUM which match the best known lower bounds for static data structure problems. Moreover, we show that our lower bound generalizes to a range of geometric problems, such as three points on a line, polygon containment, and others.

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