Tianhang Zheng (University of Missouri-Kansas City), Baochun Li (University of Toronto)

Recent work in ICML’22 established a connection between dataset condensation (DC) and differential privacy (DP), which is unfortunately problematic. To correctly connect DC and DP, we propose two differentially private dataset condensation (DPDC) algorithms—LDPDC and NDPDC. LDPDC is a linear DC algorithm that can be executed on a low-end Central Processing Unit (CPU), while NDPDC is a nonlinear DC algorithm that leverages neural networks to extract and match the latent representations between real and synthetic data. Through extensive evaluations, we demonstrate that LDPDC has comparable performance to recent DP generative methods despite its simplicity. NDPDC provides acceptable DP guarantees with a mild utility loss, compared to distribution matching (DM). Additionally, NDPDC allows a flexible trade-off between the synthetic data utility and DP budget.

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Ryo Suzuki (Keio University), Takami Sato (University of California, Irvine), Yuki Hayakawa, Kazuma Ikeda, Ozora Sako, Rokuto Nagata (Keio University), Qi Alfred Chen (University of California, Irvine), Kentaro Yoshioka (Keio University)

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Beomjin Jin (Sungkyunkwan University), Eunsoo Kim (Sungkyunkwan University), Hyunwoo Lee (KENTECH), Elisa Bertino (Purdue University), Doowon Kim (University of Tennessee, Knoxville), Hyoungshick Kim (Sungkyunkwan University)

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Hao Zhou (The Hong Kong Polytechnic University), Shuohan Wu (The Hong Kong Polytechnic University), Chenxiong Qian (University of Hong Kong), Xiapu Luo (The Hong Kong Polytechnic University), Haipeng Cai (Washington State University), Chao Zhang (Tsinghua University)

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Architecting Trigger-Action Platforms for Security, Performance and Functionality

Deepak Sirone Jegan (University of Wisconsin-Madison), Michael Swift (University of Wisconsin-Madison), Earlence Fernandes (University of California San Diego)

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