{"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/protecting-sensory-data-against-sensitive","title":"Protecting Sensory Data against Sensitive Inferences","arxiv_id":"1802.07802","date":"2018-02-21","proceeding":null,"authors":["Mohammad Malekzadeh","Richard G. Clegg","Andrea Cavallaro","Hamed Haddadi"],"abstract":"There is growing concern about how personal data are used when users grant\napplications direct access to the sensors of their mobile devices. In fact,\nhigh resolution temporal data generated by motion sensors reflect directly the\nactivities of a user and indirectly physical and demographic attributes. In\nthis paper, we propose a feature learning architecture for mobile devices that\nprovides flexible and negotiable privacy-preserving sensor data transmission by\nappropriately transforming raw sensor data. The objective is to move from the\ncurrent binary setting of granting or not permission to an application, toward\na model that allows users to grant each application permission over a limited\nrange of inferences according to the provided services. The internal structure\nof each component of the proposed architecture can be flexibly changed and the\ntrade-off between privacy and utility can be negotiated between the constraints\nof the user and the underlying application. We validated the proposed\narchitecture in an activity recognition application using two real-world\ndatasets, with the objective of recognizing an activity without disclosing\ngender as an example of private information. Results show that the proposed\nframework maintains the usefulness of the transformed data for activity\nrecognition, with an average loss of only around three percentage points, while\nreducing the possibility of gender classification to around 50\\%, the target\nrandom guess, from more than 90\\% when using raw sensor data. We also present\nand distribute MotionSense, a new dataset for activity and attribute\nrecognition collected from motion sensors.","url_abs":"http://arxiv.org/abs/1802.07802v4","url_pdf":"http://arxiv.org/pdf/1802.07802v4.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":"protecting-sensory-data-against-sensitive","repo_url":"https://github.com/mmalekzadeh/motion-sense","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"gender-classification","task_name":"Gender Classification"},{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"}],"methods":[],"datasets_introduced":[{"slug":"motionsense","name":"MotionSense","full_name":null}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.07802","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.07802"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mmalekzadeh/motion-sense","reach":null}],"summary":{"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"16186c7b71bfb065","entry":"set_data_types","repo":"mmalekzadeh/motion-sense","repo_kind":"official","path":"msda/dataset_builder.py","file_url":"https://github.com/mmalekzadeh/motion-sense/blob/HEAD/msda/dataset_builder.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"16186c7b71bfb065"}},{"code_sha256_prefix":"c88b78981ddc30be","entry":"ts_to_secs","repo":"mmalekzadeh/motion-sense","repo_kind":"official","path":"msda/dataset_builder.py","file_url":"https://github.com/mmalekzadeh/motion-sense/blob/HEAD/msda/dataset_builder.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c88b78981ddc30be"}},{"code_sha256_prefix":"7842790dca76ede1","entry":"creat_time_series","repo":"mmalekzadeh/motion-sense","repo_kind":"official","path":"msda/dataset_builder.py","file_url":"https://github.com/mmalekzadeh/motion-sense/blob/HEAD/msda/dataset_builder.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7842790dca76ede1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}