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**Published in:** LIPIcs, Volume 287, 15th Innovations in Theoretical Computer Science Conference (ITCS 2024)

Differential obliviousness (DO) is a privacy notion which mandates that the access patterns of a program satisfy differential privacy. Earlier works have shown that in numerous applications, differential obliviousness allows us to circumvent fundamental barriers pertaining to fully oblivious algorithms, resulting in asymptotical (and sometimes even polynomial) performance improvements. Although DO has been applied to various contexts, including the design of algorithms, data structures, and protocols, its compositional properties are not explored until the recent work of Zhou et al. (Eurocrypt'23). Specifically, Zhou et al. showed that the original DO notion is not composable. They then proposed a refinement of DO called neighbor-preserving differential obliviousness (NPDO), and proved a basic composition for NPDO.
In Zhou et al.’s basic composition theorem for NPDO, the privacy loss is linear in k for k-fold composition. In comparison, for standard differential privacy, we can enjoy roughly √k loss for k-fold composition by applying the well-known advanced composition theorem given an appropriate parameter range. Therefore, a natural question left open by their work is whether we can also prove an analogous advanced composition for NPDO.
In this paper, we answer this question affirmatively. As a key step in proving an advanced composition theorem for NPDO, we define a more operational notion called symmetric NPDO which we prove to be equivalent to NPDO. Using symmetric NPDO as a stepping stone, we also show how to generalize NPDO to more general notions of divergence, resulting in Rényi-NPDO, zero-concentrated-NPDO, Gassian-NPDO, and g-NPDO notions. We also prove composition theorems for these generalized notions of NPDO.

Mingxun Zhou, Mengshi Zhao, T-H. Hubert Chan, and Elaine Shi. Advanced Composition Theorems for Differential Obliviousness. In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 103:1-103:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{zhou_et_al:LIPIcs.ITCS.2024.103, author = {Zhou, Mingxun and Zhao, Mengshi and Chan, T-H. Hubert and Shi, Elaine}, title = {{Advanced Composition Theorems for Differential Obliviousness}}, booktitle = {15th Innovations in Theoretical Computer Science Conference (ITCS 2024)}, pages = {103:1--103:24}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-309-6}, ISSN = {1868-8969}, year = {2024}, volume = {287}, editor = {Guruswami, Venkatesan}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2024.103}, URN = {urn:nbn:de:0030-drops-196315}, doi = {10.4230/LIPIcs.ITCS.2024.103}, annote = {Keywords: Differential Privacy, Oblivious Algorithms} }

Document

**Published in:** LIPIcs, Volume 199, 2nd Conference on Information-Theoretic Cryptography (ITC 2021)

Oblivious RAM (ORAM) is a technique for compiling any RAM program to an oblivious counterpart, i.e., one whose access patterns do not leak information about the secret inputs. Similarly, Oblivious Parallel RAM (OPRAM) compiles a parallel RAM program to an oblivious counterpart. In this paper, we care about ORAM/OPRAM with perfect security, i.e., the access patterns must be identically distributed no matter what the program’s memory request sequence is. In the past, two types of perfect ORAMs/OPRAMs have been considered: constructions whose performance bounds hold in expectation (but may occasionally run more slowly); and constructions whose performance bounds hold deterministically (even though the algorithms themselves are randomized).
In this paper, we revisit the performance metrics for perfect ORAM/OPRAM, and show novel constructions that achieve asymptotical improvements for all performance metrics. Our first result is a new perfectly secure OPRAM scheme with O(log³ N/log log N) expected overhead. In comparison, prior literature has been stuck at O(log³ N) for more than a decade.
Next, we show how to construct a perfect ORAM with O(log³ N/log log N) deterministic simulation overhead. We further show how to make the scheme parallel, resulting in an perfect OPRAM with O(log⁴ N/log log N) deterministic simulation overhead. For perfect ORAMs/OPRAMs with deterministic performance bounds, our results achieve subexponential improvement over the state-of-the-art. Specifically, the best known prior scheme incurs more than √N deterministic simulation overhead (Raskin and Simkin, Asiacrypt'19); moreover, their scheme works only for the sequential setting and is not amenable to parallelization.
Finally, we additionally consider perfect ORAMs/OPRAMs whose performance bounds hold with high probability. For this new performance metric, we show new constructions whose simulation overhead is upper bounded by O(log³ /log log N) except with negligible in N probability, i.e., we prove high-probability performance bounds that match the expected bounds mentioned earlier.

T-H. Hubert Chan, Elaine Shi, Wei-Kai Lin, and Kartik Nayak. Perfectly Oblivious (Parallel) RAM Revisited, and Improved Constructions. In 2nd Conference on Information-Theoretic Cryptography (ITC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 199, pp. 8:1-8:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{chan_et_al:LIPIcs.ITC.2021.8, author = {Chan, T-H. Hubert and Shi, Elaine and Lin, Wei-Kai and Nayak, Kartik}, title = {{Perfectly Oblivious (Parallel) RAM Revisited, and Improved Constructions}}, booktitle = {2nd Conference on Information-Theoretic Cryptography (ITC 2021)}, pages = {8:1--8:23}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-197-9}, ISSN = {1868-8969}, year = {2021}, volume = {199}, editor = {Tessaro, Stefano}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITC.2021.8}, URN = {urn:nbn:de:0030-drops-143271}, doi = {10.4230/LIPIcs.ITC.2021.8}, annote = {Keywords: perfect oblivious RAM, oblivious PRAM} }

Document

**Published in:** LIPIcs, Volume 199, 2nd Conference on Information-Theoretic Cryptography (ITC 2021)

Numerous high-profile works have shown that access patterns to even encrypted databases can leak secret information and sometimes even lead to reconstruction of the entire database. To thwart access pattern leakage, the literature has focused on oblivious algorithms, where obliviousness requires that the access patterns leak nothing about the input data.
In this paper, we consider the Join operator, an important database primitive that has been extensively studied and optimized. Unfortunately, any fully oblivious Join algorithm would require always padding the result to the worst-case length which is quadratic in the data size N. In comparison, an insecure baseline incurs only O(R + N) cost where R is the true result length, and in the common case in practice, R is relatively short. As a typical example, when R = O(N), any fully oblivious algorithm must inherently incur a prohibitive, N-fold slowdown relative to the insecure baseline. Indeed, the (non-private) database and algorithms literature invariably focuses on studying the instance-specific rather than worst-case performance of database algorithms. Unfortunately, the stringent notion of full obliviousness precludes the design of efficient algorithms with non-trivial instance-specific performance.
To overcome this worst-case performance barrier of full obliviousness and enable algorithms with good instance-specific performance, we consider a relaxed notion of access pattern privacy called (ε, δ)-differential obliviousness (DO), originally proposed in the seminal work of Chan et al. (SODA'19). Rather than insisting that the access patterns leak no information whatsoever, the relaxed DO notion requires that the access patterns satisfy (ε, δ)-differential privacy. We show that by adopting the relaxed DO notion, we can obtain efficient database Join mechanisms whose instance-specific performance approximately matches the insecure baseline, while still offering a meaningful notion of privacy to individual users. Complementing our upper bound results, we also prove new lower bounds regarding the performance of any DO Join algorithm.
Differential obliviousness (DO) is a new notion and is a relatively unexplored territory. Following the pioneering investigations by Chan et al. and others, our work is among the very first to formally explore how DO can help overcome the worst-case performance curse of full obliviousness; moreover, we motivate our work with database applications. Our work shows new evidence why DO might be a promising notion, and opens up several exciting future directions.

Shumo Chu, Danyang Zhuo, Elaine Shi, and T-H. Hubert Chan. Differentially Oblivious Database Joins: Overcoming the Worst-Case Curse of Fully Oblivious Algorithms. In 2nd Conference on Information-Theoretic Cryptography (ITC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 199, pp. 19:1-19:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{chu_et_al:LIPIcs.ITC.2021.19, author = {Chu, Shumo and Zhuo, Danyang and Shi, Elaine and Chan, T-H. Hubert}, title = {{Differentially Oblivious Database Joins: Overcoming the Worst-Case Curse of Fully Oblivious Algorithms}}, booktitle = {2nd Conference on Information-Theoretic Cryptography (ITC 2021)}, pages = {19:1--19:24}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-197-9}, ISSN = {1868-8969}, year = {2021}, volume = {199}, editor = {Tessaro, Stefano}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITC.2021.19}, URN = {urn:nbn:de:0030-drops-143386}, doi = {10.4230/LIPIcs.ITC.2021.19}, annote = {Keywords: differentially oblivious, database join, instance-specific performance} }

Document

**Published in:** LIPIcs, Volume 151, 11th Innovations in Theoretical Computer Science Conference (ITCS 2020)

Massively Parallel Computation (MPC) is a model of computation widely believed to best capture realistic parallel computing architectures such as large-scale MapReduce and Hadoop clusters. Motivated by the fact that many data analytics tasks performed on these platforms involve sensitive user data, we initiate the theoretical exploration of how to leverage MPC architectures to enable efficient, privacy-preserving computation over massive data. Clearly if a computation task does not lend itself to an efficient implementation on MPC even without security, then we cannot hope to compute it efficiently on MPC with security. We show, on the other hand, that any task that can be efficiently computed on MPC can also be securely computed with comparable efficiency. Specifically, we show the following results:
- any MPC algorithm can be compiled to a communication-oblivious counterpart while asymptotically preserving its round and space complexity, where communication-obliviousness ensures that any network intermediary observing the communication patterns learn no information about the secret inputs;
- assuming the existence of Fully Homomorphic Encryption with a suitable notion of compactness and other standard cryptographic assumptions, any MPC algorithm can be compiled to a secure counterpart that defends against an adversary who controls not only intermediate network routers but additionally up to 1/3 - η fraction of machines (for an arbitrarily small constant η) - moreover, this compilation preserves the round complexity tightly, and preserves the space complexity upto a multiplicative security parameter related blowup.
As an initial exploration of this important direction, our work suggests new definitions and proposes novel protocols that blend algorithmic and cryptographic techniques.

T-H. Hubert Chan, Kai-Min Chung, Wei-Kai Lin, and Elaine Shi. MPC for MPC: Secure Computation on a Massively Parallel Computing Architecture. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 151, pp. 75:1-75:52, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{chan_et_al:LIPIcs.ITCS.2020.75, author = {Chan, T-H. Hubert and Chung, Kai-Min and Lin, Wei-Kai and Shi, Elaine}, title = {{MPC for MPC: Secure Computation on a Massively Parallel Computing Architecture}}, booktitle = {11th Innovations in Theoretical Computer Science Conference (ITCS 2020)}, pages = {75:1--75:52}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-134-4}, ISSN = {1868-8969}, year = {2020}, volume = {151}, editor = {Vidick, Thomas}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2020.75}, URN = {urn:nbn:de:0030-drops-117600}, doi = {10.4230/LIPIcs.ITCS.2020.75}, annote = {Keywords: massively parallel computation, secure multi-party computation} }

Document

**Published in:** LIPIcs, Volume 112, 26th Annual European Symposium on Algorithms (ESA 2018)

We present a unified (randomized) polynomial-time approximation scheme (PTAS) for the prize collecting traveling salesman problem (PCTSP) and the prize collecting Steiner tree problem (PCSTP) in doubling metrics. Given a metric space and a penalty function on a subset of points known as terminals, a solution is a subgraph on points in the metric space, whose cost is the weight of its edges plus the penalty due to terminals not covered by the subgraph. Under our unified framework, the solution subgraph needs to be Eulerian for PCTSP, while it needs to be a tree for PCSTP. Before our work, even a QPTAS for the problems in doubling metrics is not known.
Our unified PTAS is based on the previous dynamic programming frameworks proposed in [Talwar STOC 2004] and [Bartal, Gottlieb, Krauthgamer STOC 2012]. However, since it is unknown which part of the optimal cost is due to edge lengths and which part is due to penalties of uncovered terminals, we need to develop new techniques to apply previous divide-and-conquer strategies and sparse instance decompositions.

T-H. Hubert Chan, Haotian Jiang, and Shaofeng H.-C. Jiang. A Unified PTAS for Prize Collecting TSP and Steiner Tree Problem in Doubling Metrics. In 26th Annual European Symposium on Algorithms (ESA 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 112, pp. 15:1-15:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{chan_et_al:LIPIcs.ESA.2018.15, author = {Chan, T-H. Hubert and Jiang, Haotian and Jiang, Shaofeng H.-C.}, title = {{A Unified PTAS for Prize Collecting TSP and Steiner Tree Problem in Doubling Metrics}}, booktitle = {26th Annual European Symposium on Algorithms (ESA 2018)}, pages = {15:1--15:13}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-081-1}, ISSN = {1868-8969}, year = {2018}, volume = {112}, editor = {Azar, Yossi and Bast, Hannah and Herman, Grzegorz}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2018.15}, URN = {urn:nbn:de:0030-drops-94781}, doi = {10.4230/LIPIcs.ESA.2018.15}, annote = {Keywords: Doubling Dimension, Traveling Salesman Problem, Polynomial Time Approximation Scheme, Steiner Tree Problem, Prize Collecting} }

Document

**Published in:** LIPIcs, Volume 64, 27th International Symposium on Algorithms and Computation (ISAAC 2016)

We study the max-min fair allocation problem in which a set of m indivisible items are to be distributed among n agents such that the minimum utility among all agents is maximized. In the restricted setting, the utility of each item j on agent i is either 0 or some non-negative weight w_j. For this setting, Asadpour et al. [TALG, 2012] showed that a certain configuration-LP can be used to estimate the optimal value within a factor of 4 + delta, for any delta > 0, which was recently extended by Annamalai et al. [SODA 2015] to give a polynomial-time 13-approximation algorithm for the problem. For hardness results, Bezáková and Dani [SIGecom Exch., 2005] showed that it is NP-hard to approximate the problem within any ratio smaller than 2.
In this paper we consider the (1, epsilon)-restricted max-min fair allocation problem, in which for some parameter epsilon in (0, 1), each item j is either heavy (w_j = 1) or light (w_j = epsilon). We show that the (1, epsilon)-restricted case is also NP-hard to approximate within any ratio smaller than 2. Hence, this simple special case is still algorithmically interesting.
Using the configuration-LP, we are able to estimate the optimal value of the problem within a factor of 3 + delta, for any delta > 0. Extending this idea, we also obtain a quasi-polynomial time (3 + 4 epsilon)-approximation algorithm and a polynomial time 9-approximation algorithm. Moreover, we show that as epsilon tends to 0, the approximation ratio of our polynomial-time algorithm approaches 3 + 2 sqrt{2} approx 5.83.

T-H. Hubert Chan, Zhihao Gavin Tang, and Xiaowei Wu. On (1, epsilon)-Restricted Max-Min Fair Allocation Problem. In 27th International Symposium on Algorithms and Computation (ISAAC 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 64, pp. 23:1-23:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{chan_et_al:LIPIcs.ISAAC.2016.23, author = {Chan, T-H. Hubert and Tang, Zhihao Gavin and Wu, Xiaowei}, title = {{On (1, epsilon)-Restricted Max-Min Fair Allocation Problem}}, booktitle = {27th International Symposium on Algorithms and Computation (ISAAC 2016)}, pages = {23:1--23:13}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-026-2}, ISSN = {1868-8969}, year = {2016}, volume = {64}, editor = {Hong, Seok-Hee}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2016.23}, URN = {urn:nbn:de:0030-drops-67939}, doi = {10.4230/LIPIcs.ISAAC.2016.23}, annote = {Keywords: Max-Min Fair Allocation, Hypergraph Matching} }

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