{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T15:51:28Z","timestamp":1789141888500,"version":"build-2803163510"},"reference-count":18,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:p>\n            In this work, we demonstrate Sterling, a decentralized marketplace for private data. Sterling enables privacy-preserving distribution and use of data by using\n            <jats:italic>privacy-preserving smart contracts<\/jats:italic>\n            which run on a permissionless blockchain. The privacy-preserving smart contracts, written by data providers and consumers, immutably and irrevocably represent the interests of their creators. In particular, we provide a mechanism for data providers to control the use of their data through automatic verification of data consumer contracts, allowing providers to express constraints such as pricing and differential privacy. Through smart contracts and trusted execution environments, Sterling enables privacy-preserving analytics and machine learning over private data in an efficient manner. The resulting economy ensures that the interests of all parties are aligned.\n          <\/jats:p>\n          <jats:p>For the demonstration, we highlight the use of Sterling for training machine learning models on individuals' health data. In doing so, we showcase novel approaches to automatically appraising training data, verifying and enforcing model privacy properties, and efficiently training private models on the blockchain using trusted hardware.<\/jats:p>","DOI":"10.14778\/3229863.3236266","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"2086-2089","source":"Crossref","is-referenced-by-count":78,"title":["A demonstration of sterling"],"prefix":"10.14778","volume":"11","author":[{"given":"Nick","family":"Hynes","sequence":"first","affiliation":[{"name":"Oasis Labs and UC Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Dao","sequence":"additional","affiliation":[{"name":"UC Berkeley and ETH Zurich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Yan","sequence":"additional","affiliation":[{"name":"Oasis Labs"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond","family":"Cheng","sequence":"additional","affiliation":[{"name":"Oasis Labs and UC Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawn","family":"Song","sequence":"additional","affiliation":[{"name":"Oasis Labs and UC Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_2_1_2_1","volume-title":"HASP.","author":"Ittai Anati","year":"2013"},{"key":"e_1_2_1_3_1","unstructured":"Nicholas Carlini et al. \"The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets\". In: arXiv:1802.08232 (2018).  Nicholas Carlini et al. \"The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets\". In: arXiv:1802.08232 (2018)."},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Raymond Cheng et al. \"Ekiden: A Platform for Confidentiality-Preserving Trustworthy and Performant Smart Contract Execution\". In: arXiv:1804.05141 (2018).  Raymond Cheng et al. \"Ekiden: A Platform for Confidentiality-Preserving Trustworthy and Performant Smart Contract Execution\". In: arXiv:1804.05141 (2018).","DOI":"10.1109\/EuroSP.2019.00023"},{"key":"e_1_2_1_5_1","unstructured":"D. Dao et al. \"DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation\". In: arXiv:1802.04780 (2018).  D. Dao et al. \"DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation\". In: arXiv:1802.04780 (2018)."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2010.12"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2229012.2229054"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2014.35"},{"key":"e_1_2_1_9_1","volume-title":"arXiv","author":"Hynes Nick","year":"1801"},{"key":"e_1_2_1_10_1","unstructured":"Ruoxi Jia et al. \"'How Much is My Data Worth?\": Data Valuation with Efficient Shapley Value Estimation\". 2018.  Ruoxi Jia et al. \"'How Much is My Data Worth?\": Data Valuation with Efficient Shapley Value Estimation\". 2018."},{"key":"e_1_2_1_11_1","unstructured":"Keystone Project. 2018. URL: https:\/\/keystone-enclave.org\/. Keystone Project . 2018. URL: https:\/\/keystone-enclave.org\/."},{"key":"e_1_2_1_12_1","first-page":"1885","author":"Koh Pang Wei","year":"2017","journal-title":"ICML."},{"key":"e_1_2_1_13_1","first-page":"427","author":"Lebedev Ilia A.","year":"2018","journal-title":"IACR."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559850"},{"key":"e_1_2_1_15_1","unstructured":"Joe Near et al. \"Optio: Differential Privacy for Machine Learning Pipelines Statically and Automatically\". 2018.  Joe Near et al. \"Optio: Differential Privacy for Machine Learning Pipelines Statically and Automatically\". 2018."},{"key":"e_1_2_1_16_1","unstructured":"Oasis Blockchain. 2018. URL: https:\/\/www.oasislabs.com\/. Oasis Blockchain . 2018. URL: https:\/\/www.oasislabs.com\/."},{"key":"e_1_2_1_17_1","first-page":"619","author":"Oblivious Multi-Party Machine Olga Ohrimenko","year":"2016","journal-title":"USENIX Security."},{"key":"e_1_2_1_18_1","unstructured":"Alvin Rajkomar et al. \"Scalable and accurate deep learning for electronic health records\". In: arXiv:1801.07860 (2018).  Alvin Rajkomar et al. \"Scalable and accurate deep learning for electronic health records\". In: arXiv:1801.07860 (2018)."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3229863.3236266","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:13:24Z","timestamp":1672222404000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3229863.3236266"}},"subtitle":["a privacy-preserving data marketplace"],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":18,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.14778\/3229863.3236266"],"URL":"https:\/\/doi.org\/10.14778\/3229863.3236266","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}