{"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/deciding-differential-privacy-of-online","title":"Deciding Differential Privacy of Online Algorithms with Multiple Variables","arxiv_id":"2309.06615","date":"2023-09-12","proceeding":null,"authors":["Rohit Chadha","A. Prasad Sistla","Mahesh Viswanathan","Bishnu Bhusal"],"abstract":"We consider the problem of checking the differential privacy of online randomized algorithms that process a stream of inputs and produce outputs corresponding to each input. This paper generalizes an automaton model called DiP automata (See arXiv:2104.14519) to describe such algorithms by allowing multiple real-valued storage variables. A DiP automaton is a parametric automaton whose behavior depends on the privacy budget $\\epsilon$. An automaton $A$ will be said to be differentially private if, for some $\\mathfrak{D}$, the automaton is $\\mathfrak{D}\\epsilon$-differentially private for all values of $\\epsilon>0$. We identify a precise characterization of the class of all differentially private DiP automata. We show that the problem of determining if a given DiP automaton belongs to this class is PSPACE-complete. Our PSPACE algorithm also computes a value for $\\mathfrak{D}$ when the given automaton is differentially private. The algorithm has been implemented, and experiments demonstrating its effectiveness are presented.","url_abs":"https://arxiv.org/abs/2309.06615v1","url_pdf":"https://arxiv.org/pdf/2309.06615v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"deciding-differential-privacy-of-online","repo_url":"https://github.com/bhusalb/DiPAut","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}