Bringing privacy control back to citizens

Stach, Christoph; Gritti, Clémentine; Mitschang, Bernhard

doi:10.1145/3341105.3375754

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…Prinseps captures users' privacy needs in the form of a privacy policy. The literature shows various requirements for a user-defined privacy policy [13,25]-we extract the most relevant for Prinseps in Table 1. We create the privacy policy based on static and dynamic rules [25], as explained using our running example.…”

Section: R4: Awarenessmentioning

confidence: 99%

“…The literature shows various requirements for a user-defined privacy policy [13,25]-we extract the most relevant for Prinseps in Table 1. We create the privacy policy based on static and dynamic rules [25], as explained using our running example. Respecting R1: Transparency, a HAR application must brief Bob that IMU data from his wearables is collected to monitor his exercise.…”

Section: R4: Awarenessmentioning

confidence: 99%

“…The data streams that originate by sensing users or their environment inherently contain sensitive information, e.g., user's lifestyle, requiring SPSs that process such data to comply with privacy regulations, like the GDPR [25]. As crucially, users start to increasingly demand stricter control over the data collected by IoT applications about them [13].…”

Section: Introductionmentioning

confidence: 99%

“…Contributions. The topic of privacy protection in SPSs has recently emerged, yet the privacy issues stemming from SPS's multilayeredness remain underexplored [19,25]. As such, the privacy-preserving mechanisms (PPMs) utilized in SPSs are typically adopted from the database community, requiring a major redesign to satisfy the realtime nature of SPSs [18], while several PPMs developed specifically for SPSs are still not mature, which makes it difficult to holistically apply them in SPSs [25].…”

Section: Introductionmentioning

confidence: 99%

“…The topic of privacy protection in SPSs has recently emerged, yet the privacy issues stemming from SPS's multilayeredness remain underexplored [19,25]. As such, the privacy-preserving mechanisms (PPMs) utilized in SPSs are typically adopted from the database community, requiring a major redesign to satisfy the realtime nature of SPSs [18], while several PPMs developed specifically for SPSs are still not mature, which makes it difficult to holistically apply them in SPSs [25]. We identify three challenges of using existing PPMs in multilayered SPSs: (1) threats to user privacy are not systematically understood in these SPSs, complicating the selection of appropriate PPMs; (2) PPMs must not only be applied holistically, i.e., at each layer of the multilayered SPS to provide strong privacy protection, but also satisfy real-time needs of SPSs, imposing little overhead; (3) the PPMs, selected and applied in this fashion, should enable a privacy-utility tradeoff (PUT), which is tunable depending on SPS's user privacy needs and application quality of service (QoS) requirements.…”

Section: Introductionmentioning

confidence: 99%

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No One Size (PPM) Fits All: Towards Privacy in Stream Processing Systems

Fomichev

Luthra

Benndorf

et al. 2023

Proceedings of the 17th ACM International Conference on Distributed and Event-Based Systems

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Stream processing systems (SPSs) have been designed to process data streams in real-time, allowing organizations to analyze and act upon data on-the-fly, as it is generated. However, handling sensitive or personal data in these multilayered SPSs that distribute resources across sensor, fog, and cloud layers raises privacy concerns, as the data may be subject to unauthorized access and attacks that can violate user privacy, hence facing regulations such as the GDPR across the SPS layers. To address these issues, different privacy-preserving mechanisms (PPMs) are proposed to protect user privacy in SPSs. Yet, selecting and applying such PPMs in SPSs is challenging, since they must operate in real-time while tolerating little overhead. The multilayered nature of SPSs complicates privacy protection because each layer may confront different privacy threats, which must be addressed by specific PPMs. To overcome these challenges, we present Prinseps, our comprehensive privacy vision for SPSs. Towards this vision, we (1) identify critical privacy threats on different layers of the multilayered SPS, (2) evaluate the effectiveness of existing PPMs in addressing such threats, and (3) integrate privacy considerations into the decision-making processes of SPSs. CCS CONCEPTS• Security and privacy → Privacy protections; • Information systems → Stream management.

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Section: R4: Awarenessmentioning

confidence: 99%