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However, as the use of DNNs expands, protecting user input privacy has become increasingly important. Herein, a potential security vulnerability is identified wherein an adversary can reconstruct the user's private input data from a power side\u2010channel attack even without knowledge of the stored DNN model. An attack approach using a generative adversarial network is developed to achieve high\u2010quality data reconstruction from power leakage measurements. The analyses show that the attack methodology is effective in reconstructing user input data from power leakage of the analog CIM accelerator, even at large noise levels and after countermeasures. To demonstrate the efficacy of the proposed approach, an example of CIM inference of U\u2010Net for brain tumor detection is attacked, and the original magnetic resonance imaging medical images can be successfully reconstructed even at a noise level of 20% standard deviation of the maximum power signal value. This study highlights a potential security vulnerability in emerging analog CIM accelerators and raises awareness of needed safety features to protect user privacy in such systems.<\/jats:p>","DOI":"10.1002\/aisy.202300313","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T23:05:48Z","timestamp":1695423948000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["PowerGAN: A Machine Learning Approach for Power Side\u2010Channel Attack on Compute\u2010in\u2010Memory Accelerators"],"prefix":"10.1002","volume":"5","author":[{"given":"Ziyu","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science University of Michigan  Ann Arbor MI 48109 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuting","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science University of Michigan  Ann Arbor MI 48109 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongmo","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science University of Michigan  Ann Arbor MI 48109 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangmin","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science University of Michigan  Ann Arbor MI 48109 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science University of Michigan  Ann Arbor MI 48109 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason K.","family":"Eshraghian","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science University of Michigan  Ann Arbor MI 48109 USA"},{"name":"Department of Electrical and Computer Engineering University of California  Santa Cruz CA 95064 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4731-1976","authenticated-orcid":false,"given":"Wei D.","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science University of Michigan  Ann Arbor MI 48109 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"a)OpenAI (Preprint) arXiv:2303.08774 v3 submitted: May2023;"},{"key":"e_1_2_10_2_2","unstructured":"b)K.He X.Zhang S.Ren J.Sun(Preprint) arXiv:1512.03385 v1 submitted: Dec2015;"},{"key":"e_1_2_10_2_3","unstructured":"c)E.Variani X.Lei E.McDermott I. 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