{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/privacy-preserving-clustering-a-new","title":"Privacy-Preserving Clustering: A New ApproachBased on Invariant Order Encryption","arxiv_id":null,"date":"2020-12-20","proceeding":null,"authors":["Mihail-Iulian Pleșa","Cezar Pleșca"],"abstract":"Cloud  computing  is  increasingly  used.  One  main  use  of  cloud  computing  is  the  running  of  a  machine  learning  algorithm.  Due  to the  large  amount  of  data  required  for  these  algorithms,  they  can  no  longer  be  run  on  personal  computers.  Uploading  personal  data  to  the  cloud  automatically  raises  the  issues  of  confidentiality  of  this  data.  In  this  paper,  we  show  through   a   series   of   experiments   that   an   order-preserving encryption algorithm can    be    applied    to    guarantee    the    confidentiality   of   the   input   of   two   well-known   clustering   algorithms: K-Means  and  DBSCAN.  We  show  that  K-Means can be modified to be applied over the encrypted data. We also proposed    a    slight    improvement    to    an    order-preserving encryption  scheme  to  ensure  that  it  is  randomized,  therefore increasing   its   security   level.   Finally,   after   studying   the   performance  of  clustering  algorithms  over  encrypted  data  we  show  a  practical  application  of  this  idea,  namely  the  color reduction over an encrypted image.","url_abs":"https://jmiltechnol.mta.ro/6/10_PLESA,%20PLESCA-min.pdf","url_pdf":"https://jmiltechnol.mta.ro/6/10_PLESA,%20PLESCA-min.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"privacy-preserving-clustering-a-new","repo_url":"https://github.com/miiip/OPEML","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cloud-computing","task_name":"Cloud Computing"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"}],"methods":[{"method_slug":"k-means-clustering","method_name":"k-Means Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}