Anonysense: privacy-aware people-centric sensing

By Cory Cornelius, Apu Kapadia, David F. Kotz, D. Peebles, Minho Shin, Nikos Triandopoulos. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2008

460 citations recorded by the source corpus.

Personal mobile devices are increasingly equipped with the capability to sense the physical world (through cameras, microphones, and accelerometers, for example) and the, network world (with Wi-Fi and Bluetooth interfaces). Such devices offer many new opportunities for cooperative sensing applications. For example, users' mobile phones may contribute data to community-oriented information services, from city-wide pollution monitoring to enterprise-wide detection of unauthorized Wi-Fi access points. This people-centric mobile-sensing model introduces a new security challenge in the design of mobile systems: protecting the privacy of participants while allowing their devices to reliably contribute high-quality data to these large-scale applications. We describe AnonySense, a privacy-aware architecture for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our trust model, and the security properties that drove the design of the AnonySense system. We evaluate our prototype implementation through experiments that indicate the feasibility of this approach, and through two applications: a Wi-Fi rogue access point detector and a lost-object finder.

Subfields: Computer Science Applications, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f594689291ddf723f58cb42942e0af9284eea9eb

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Citations in the corpus

PRISM: platform for remote sensing using smartphones (2010, methodology): “Push-based Model: Applications can be deployed on mobile phones in two ways: a pull-based approach [13] where all mobile clients independently pull/download jobs from a server or a push-based approach where a server pushes jobs to only a desired set of mobile phones.”
P3: Toward Privacy-Preserving Photo Sharing (2013, unknown): “Tangentially related is a body of work in the computer systems community on ensuring other forms of privacy: secure distributed storage systems [23, 37, 11], and privacy and anonymity for mobile systems [19, 26, 16].”
Measuring Large-Scale Social Networks with High Resolution (2014, methodology): “A large number of solutions for sensor-driven human data collection have been developed, ranging from dedicated software to complete platforms, notably ContextPhone [20], SocioXensor [21], MyExpe-rience [22], Anonysense [23], CenceMe [24], Cityware [25], Darwin phones [26], Vita [27], and Context-To”
A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing (2014, background): “While location privacy has largely been studied in the context of location-based services, only a few work have studied privacy in participatory sensing (PS) [14, 12, 13, 5].”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, methodology): “Therefore, a privacy-preserving training process in [49], [21], [19] is orthogonal to our scenario.”
Mobile Crowd Sensing and Computing (2015, background): “One such effort is the AnonySense architecture proposed in Cornelius et al. [2008], which supported the development of privacy-aware applications based on crowd sensing.”
From participatory sensing to Mobile Crowd Sensing (2014, background): “For example, in [18], a task description language called AnonyTL was proposed to specify the sample context for a sensing task.”