Leveraging Crowdsensed Data Streams to Discover and Sell Knowledge: A Secure and Efficient Realization

Leveraging the wisdom of crowd for knowledge discovery and monetization is increasingly popular nowadays. Among others, one popular way of leveraging the crowd wisdom is crowdsensing with truth discovery, which is able to discover truthful knowledge from the unreliable sensory data harvested from mobile clients. In order to become truly successful, however, a number of challenges are yet to be addressed. First, safeguarding clients' sensory data is demanded for privacy protection. Second, in many real crowdsensing applications, data are usually collected in a streaming manner, so truth discovery is naturally required to be efficiently conducted in a streaming fashion. Thirdly, knowledge monetization should be made full-fledged, endowed with features of transparency and streamlined processing while fully addressing the practical needs of parties in the monetization ecosystem. In this paper, we present our initial effort on a crowdsensing framework that enables privacy-preserving knowledge discovery and full-fledged blockchain-based knowledge monetization. Our framework enables privacy-preserving and efficient truth discovery over encrypted crowdsensed data streams for truthful knowledge discovery. Meanwhile, with careful integration of the newly emerging blockchain-based smart contract technology, our framework allows full-fledged knowledge monetization. Tackling the challenges of monetization fairness and (on-chain) knowledge confidentiality, our customized knowledge monetization design well respects the interests of knowledge seller and requester, with full support of transparency, streamlined processing, and automatic quality-aware rewards for clients. Extensive experiments on Microsoft Azure cloud and Ethereum blockchain demonstrate the practically affordable performance of our design.

[1]  Payman Mohassel,et al.  SecureML: A System for Scalable Privacy-Preserving Machine Learning , 2017, 2017 IEEE Symposium on Security and Privacy (SP).

[2]  Yao Lu,et al.  Oblivious Neural Network Predictions via MiniONN Transformations , 2017, IACR Cryptol. ePrint Arch..

[3]  Mauro Conti,et al.  You are AIRing too Much: Assessing the Privacy of Users in Crowdsourcing Environmental Data , 2015, 2015 IEEE Trustcom/BigDataSE/ISPA.

[4]  Mauro Barni,et al.  Oblivious Neural Network Computing via Homomorphic Encryption , 2007, EURASIP J. Inf. Secur..

[5]  Zhu Wang,et al.  Mobile Crowd Sensing and Computing , 2015, ACM Comput. Surv..

[6]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[7]  Bo Zhao,et al.  A Survey on Truth Discovery , 2015, SKDD.

[8]  Yin Wang,et al.  CrowdAtlas: self-updating maps for cloud and personal use , 2013, MobiSys '13.

[9]  Cong Wang,et al.  Harnessing the Cloud for Securely Outsourcing Large-Scale Systems of Linear Equations , 2013, IEEE Transactions on Parallel and Distributed Systems.

[10]  Cong Wang,et al.  Non-Interactive Privacy-Preserving Truth Discovery in Crowd Sensing Applications , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[11]  Jeremy Clark,et al.  SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies , 2015, 2015 IEEE Symposium on Security and Privacy.

[12]  Andrew Chi-Chih Yao,et al.  How to generate and exchange secrets , 1986, 27th Annual Symposium on Foundations of Computer Science (sfcs 1986).

[13]  Bo Zhao,et al.  On the Discovery of Evolving Truth , 2015, KDD.

[14]  Fan Zhang,et al.  Sealed-Glass Proofs: Using Transparent Enclaves to Prove and Sell Knowledge , 2017, 2017 IEEE European Symposium on Security and Privacy (EuroS&P).

[15]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[16]  Jason Teutsch,et al.  SmartPool: Practical Decentralized Pooled Mining , 2017, USENIX Security Symposium.

[17]  Yehuda Lindell,et al.  A Proof of Security of Yao’s Protocol for Two-Party Computation , 2009, Journal of Cryptology.

[18]  Stratis Ioannidis,et al.  Privacy-preserving matrix factorization , 2013, CCS.

[19]  Emiliano De Cristofaro,et al.  What's the Gist? Privacy-Preserving Aggregation of User Profiles , 2014, ESORICS.

[20]  Donald Beaver,et al.  Efficient Multiparty Protocols Using Circuit Randomization , 1991, CRYPTO.

[21]  Cong Wang,et al.  Towards trustworthy and private keyword search in encrypted decentralized storage , 2017, 2017 IEEE International Conference on Communications (ICC).

[22]  Elaine Shi,et al.  Hawk: The Blockchain Model of Cryptography and Privacy-Preserving Smart Contracts , 2016, 2016 IEEE Symposium on Security and Privacy (SP).

[23]  Aziz Mohaisen,et al.  When Smart TV Meets CRN: Privacy-Preserving Fine-Grained Spectrum Access , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[24]  Charu C. Aggarwal,et al.  Recursive Fact-Finding: A Streaming Approach to Truth Estimation in Crowdsourcing Applications , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems.

[25]  Guihai Chen,et al.  Pay as How Well You Do: A Quality Based Incentive Mechanism for Crowdsensing , 2015, MobiHoc.

[26]  Abdulsalam Yassine,et al.  Smart Meters Big Data: Game Theoretic Model for Fair Data Sharing in Deregulated Smart Grids , 2015, IEEE Access.

[27]  Cong Wang,et al.  Privacy-Aware and Efficient Mobile Crowdsensing with Truth Discovery , 2020, IEEE Transactions on Dependable and Secure Computing.

[28]  Rosario Gennaro,et al.  Zero-Knowledge Contingent Payments Revisited: Attacks and Payments for Services , 2017, IACR Cryptol. ePrint Arch..

[29]  Dan Boneh,et al.  Prio: Private, Robust, and Scalable Computation of Aggregate Statistics , 2017, NSDI.

[30]  Ran Canetti,et al.  Security and Composition of Multiparty Cryptographic Protocols , 2000, Journal of Cryptology.

[31]  Chenglin Miao,et al.  A lightweight privacy-preserving truth discovery framework for mobile crowd sensing systems , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[32]  Yu Zheng,et al.  U-Air: when urban air quality inference meets big data , 2013, KDD.

[33]  Chenglin Miao,et al.  Cloud-Enabled Privacy-Preserving Truth Discovery in Crowd Sensing Systems , 2015, SenSys.

[34]  Dongxiao Liu,et al.  Achieving efficient and privacy-preserving truth discovery in crowd sensing systems , 2017, Comput. Secur..

[35]  Fan Zhang,et al.  Solidus: Confidential Distributed Ledger Transactions via PVORM , 2017, CCS.

[36]  Kui Ren,et al.  Secure Surfing: Privacy-Preserving Speeded-Up Robust Feature Extractor , 2016, 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS).

[37]  Bo Zhao,et al.  Conflicts to Harmony: A Framework for Resolving Conflicts in Heterogeneous Data by Truth Discovery , 2016, IEEE Transactions on Knowledge and Data Engineering.