AnonySense: Opportunistic and Privacy-Preserving Context Collection

Abstract

Abstract. Opportunistic sensing allows applications to "task" mobile devices to measure context in a target region. For example, one could leverage sensorequipped vehicles to measure traffic or pollution levels on a particular street, or users' mobile phones to locate (Bluetooth-enabled) objects in their neighborhood. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk-even if a report has been anonymized, the accompanying time and location can reveal sufficient information to deanonymize the user whose device sent the report. We propose AnonySense, a general-purpose architecture for leveraging users' mobile devices for measuring context, while maintaining the privacy of the users. AnonySense features multiple layers of privacy protection-a framework for nodes to receive tasks anonymously, a novel blurring mechanism based on tessellation and clustering to protect users' privacy against the system while reporting context, and k-anonymous report aggregation to improve the users' privacy against applications receiving the context. We outline the architecture and security properties of AnonySense, and focus on evaluating our tessellation and clustering algorithm against real mobility traces.

Combating Software and Sybil Attacks to Data Integrity in Crowd-Sourced Embedded Systems

“…This can be done using pre-defined campaigns that specify the data to collect, when to collect that data, and how to aggregate it [Burke et al 2006;Kapadia et al 2008]. Then, during the course of receiving data, the portal pseudo-randomly requests the mobile proxy for the latest window of signed raw data used to compute the just received aggregate (the proxy needs to buffer this data).…”

Privacy assurance in mobile sensing networks: Go beyond trusted servers

“…If ρ does not reach the threshold τ , the node decreases ρ by 1 k , chooses a friend by random out of k-1 friends (excluding the previous node who sent the image to the node to avoid sending messages back and forth between two users.) and sends m (line [12][13][14]. If the current user only has one friend, it starts over the HP 3 algorithm (line 10).…”

“…Although the context in participatory sensing networks is different from locationbased services, the principle of k-anonymity can be used to address location privacy problems in such scenario as well. In [6], [12], the authors employed k-anonymity by generalizing k users into a tile of the spatial tessellation so that each one of them is indistinguishable from k-1 other users. Huang et al [8] extended the same idea by using a microaggregation method to find the best coordinates to represent tiles which decreased the data distortion error.…”

Privacy in Georeferenced Context-Aware Services: A Survey

“…While the above mentioned works try to protect the privacy of users accessing a remote service, the AnonySense system [48] is aimed at supporting privacy in opportunistic sensing applications, i.e., applications that leverage opportunistic networks formed by mobile devices to acquire aggregated context data in a particular region. To reach this goal, the geographic area is logically partitioned into tiles large enough to probabilistically gain k-anonymity; i.e., regions visited with high probability by more than k persons during a given time granule.…”