Abstract
Edge computing is becoming a prevalent alternative to the classical cloud paradigm. Instead of relying on a centralized infrastructure, hyper local clouds which are used in fog computing and edge clouds focus on performing computation and storing data locally. This increase of locality allows an enhancement of privacy and interactivity with end users. In particular, this allows computation to be performed near the users and thus shielding them from directed tracking. However, current computational frameworks are not suitable to implement privacy-preserving computation on the edge. Multi-party computation (MPC) poses itself as a suitable option to offer the basic building block for building decentralized privacy-preserving computational frameworks. In MPC, each party has to share their own data (inputs) with the other parties over a public function while ensuring that no private information is leaked. One of the recent approaches in this field is Enigma’s computation model based on an optimized version of secure multi-party computation which removes the need for a trusted third party. This model works in parallel with blockchain technology that controls the network, manages access control, identities, and serves as a tamper proof log of events. In this work, we follow this path of privacy based on blockchain with secure multi-party computation. We start describing the related work, then the current state of the art in terms of security and privacy and finally new directions in the field with special focus in security and privacy.
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Notes
- 1.
Bitcoin is a digital currency and online payment system, also called digital cash. It works in a decentralized way, that uses peer-to-peer to enable payments between parties without the need of mutual trust. The payments are made in Bitcoins that are digital coins issued and transferred by the Bitcoin network [1].
- 2.
Mist computing decreases latency and increases subsystems’ autonomy. This takes fog computing concepts further by pushing some of the computation to the very edge of the network, to the sensor and actuator devices that make up the network [6].
- 3.
The main innovation behind IOTA is the Tangle, a novel new blockless distributed ledger which is scalable, lightweight and for the first time ever makes it possible to transfer value without any fees. Contrary to today’s blockchains, consensus is no-longer decoupled but instead an intrinsic part of the system, leading to decentralized and self-regulating peer-to-peer network [9].
- 4.
Ethereum is a decentralized platform that runs smart contracts: applications that run exactly as programmed without any possibility of downtime, censorship, fraud or third party interference [10].
- 5.
- 6.
“Additive sharing supports efficient addition and multiplication due to the algebraic properties of the scheme. However, floating-point arithmetic is much more sophisticated and contains a composition of different operations, both integer arithmetic and bitwise operations.” [43].
- 7.
Shamir’s Secret Sharing is a form of secret sharing, where a secret is divided into parts, giving each participant a random part of the secret, where some of the parts or all of them are needed in order to reconstruct the secret. Sometimes, it is used a threshold scheme to define k parts that are sufficient to reconstruct the original secret, since can be impractical to have all participants to combine the secret [44].
References
D. Ron, A. Shamir, Quantitative analysis of the full bitcoin transaction graph, in International Conference on Financial Cryptography and Data Security (Springer, Berlin, 2013)
M. Swan, Blockchain: Blueprint for a New Economy (O’Reilly Media, Sebastopol, 2015)
A. Dorri, S.S. Kanhere, R. Jurdak, Blockchain in internet of things: challenges and solutions (2016). arXiv preprint arXiv:1608.05187
G. Zyskind, O. Nathan, A. Pentland, Enigma: decentralized computation platform with guaranteed privacy (2015). arXiv preprint arXiv:1506.03471
What is IOTA? https://iota.readme.io/v1.1.0/docs. Cited 23 January 2017
J.S. Preden et al., The benefits of self-awareness and attention in fog and mist computing. IEEE Comput. Mag. 48, 37–45 (2015)
M. Atzori, Blockchain-based architectures for the internet of things: a survey. Browser Download This Paper (2016)
IOTA: Economy of Internet-of-Things (2016). https://medium.com/@DavidSonstebo/iota-97592581f985#.rhosuii7l. Cited 10 November 2016
IOTA (2016). http://www.iotatoken.com/. Cited 10 November 2016
Ethereum - Homestead Release Blockchain App Platform (2013). https://www.ethereum.org/. Cited 11 November 2016
IOTA: Internet of Things Without the Blockchain? (2016) http://bitcoinist.net/iota-internet-things-without-blockchain/. Cited 10 November 2016
P. Veena et al., Empowering the edge-practical insights on a decentralized internet of things. IBM Institute for Business Value 17 (2015)
Autonomous Decentralized Peer-to-Peer Telemetry (2015). http://wiki.p2pfoundation.net/Autonomous_Decentralized_Peer-to-Peer_Telemetry. Cited 11 November 2016
M. Signorini, Towards an internet of trust: issues and solutions for identification and authentication in the internet of things. Ph.D Thesis, Universitat Pompeu Fabra (2015)
S. Nakamoto, Bitcoin: a peer-to-peer electronic cash system (2008). https://bitcoin.org/en/. Cited 18 April 2017
S. King, S. Nadal, Ppcoin: peer-to-peer crypto-currency with proof-of-stake. Self-published paper (2012)
TeleHash - Encrypted Mesh Protocol (2014). http://telehash.org/. Cited 11 November 2016
IBM & Samsung live demo of ADEPT — TheProtocol.TV (2015). https://www.youtube.com/watch?v=U1XOPIqyP7A. Cited 11 November 2016
A.C. Yao, Protocols for secure computations, in 23rd Annual Symposium on Foundations of Computer Science, 1982. SFCS’08 (IEEE, New York, 1982)
N.M. Edwin, Software frameworks, architectural and design patterns. J. Softw. Eng. Appl. 7 (8), 670 (2014)
I.P. Vuksanovic, B. Sudarevic, Use of web application frameworks in the development of small applications, in MIPRO, 2011 Proceedings of the 34th International Convention (IEEE, New York, 2011)
Twisted Matrix Labs: Building the engine of your Internet (2016). http://twistedmatrix.com/trac/. Cited 25 October 2016
The General Multiprecision PYthon project (GMPY) (2008). https://wiki.python.org/moin/GmPy. Cited 25 October 2016
A. Aly, Network flow problems with secure multiparty computation. Diss. Ph.D Thesis, Universté catholique de Louvain, IMMAQ (2015)
I. Damgård et al., Asynchronous multiparty computation: theory and implementation, in International Workshop on Public Key Cryptography (Springer, Berlin, 2009)
VIFF, the Virtual Ideal Functionality Framework (2007). http://viff.dk/. Cited 25 October 2016
D. Bogdanov, S. Laur, J. Willemson, Sharemind: a framework for fast privacy-preserving computations. in European Symposium on Research in Computer Security (Springer, Berlin, 2008)
Sharemind SDK Beta (2015). https://sharemind-sdk.github.io/.Cited25October2016
I. Damgård et al., Multiparty computation from somewhat homomorphic encryption, in Advances in Cryptology—CRYPTO 2012 (Springer, Berlin, 2012), pp. 643–662
Y. Lindell et al., Efficient constant round multi-party computation combining BMR and SPDZ. in Annual Cryptology Conference (Springer, Berlin, 2015)
I. Damgård et al., Practical covertly secure mpc for dishonest majority—or: Breaking the spdz limits, in European Symposium on Research in Computer Security (Springer, Berlin, 2013)
SPDZ Software (2016). https://www.cs.bris.ac.uk/Research/CryptographySecurity/SPDZ/. Cited 25 October 2016
D. Malkhi et al., Fairplay-secure two-party computation system, in USENIX Security Symposium, vol. 4 (2004)
A. Ben-David, N. Nisan, B. Pinkas, FairplayMP: a system for secure multi-party computation, in Proceedings of the 15th ACM Conference on Computer and Communications Security (ACM, New York, 2008)
SCAPI Documentation (2014). https://scapi.readthedocs.io/en/latest/intro.html. Cited 30 October 2016
W. Henecka et al., TASTY: tool for automating secure two-party computations, in Proceedings of the 17th ACM Conference on Computer and Communications Security (ACM, New York, 2010)
M. Burkhart et al., Sepia: security through private information aggregation (2009). arXiv preprint arXiv:0903.4258
M. Burkhart, M. Strasser, D. Many, X.A. Dimitropoulos, SEPIA: privacy-preserving aggregation of multi-domain network events and statistics, in USENIX Security Symposium, USENIX Association (2010), pp. 223–240
SEPIA - Security through Private Information Aggregation (2011). http://sepia.ee.ethz.ch/. Cited 10 November 2016
Y. Ejgenberg et al., SCAPI: the secure computation application programming interface. IACR Cryptol. 2012, 629 (2012). ePrint Archive
P. Chen, S. Narayanan, J. Shen, Using Secure MPC to Play Games. (Massachusetts Institute of Technology, 2015)
A.C.-C. Yao, How to generate and exchange secrets, in 27th Annual Symposium on Foundations of Computer Science, 1986 (IEEE, New York, 1986)
P. Pullonen, S. Siim, Combining secret sharing and garbled circuits for efficient private IEEE 754 floating-point computations, in International Conference on Financial Cryptography and Data Security (Springer, Berlin, 2015)
A. Shamir, How to share a secret. Commun. ACM 22 (11), 612–613 (1979)
K.V. Jónsson, G. Kreitz, M. Uddin, Secure multi-party sorting and applications. IACR Cryptol. 2011, 122 (2011). ePrint Archive
Y. Huang et al., Faster secure two-party computation using garbled circuits, in USENIX Security Symposium, vol. 201(1) (2011)
S. Havron, Poster: secure multi-party computation as a tool for privacy-preserving data analysis (University of Virginia, 2016)
M. Andrychowicz et al., Secure multiparty computations on bitcoin, in 2014 IEEE Symposium on Security and Privacy (IEEE, New York, 2014)
S. Rass, P. Schartner, M. Brodbeck, Private function evaluation by local two-party computation. EURASIP J. Inform. Secur. 2015 (1), 1–11 (2015)
B. Yuan, W. Lin, C. McDonnell, Blockchains and electronic health records(2015). http://mcdonnell.mit.edu/blockchain_ehr.pdf. Cited 18 April 2017
M. Herlihy, M. Moir, Enhancing accountability and trust in distributed ledgers (2016). arXiv preprint arXiv:1606.07490
Blockchain and Health IT: Algorithms, Privacy, and Data (2016). White Paper
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Sousa, P.R., Antunes, L., Martins, R. (2018). The Present and Future of Privacy-Preserving Computation in Fog Computing. In: Rahmani, A., Liljeberg, P., Preden, JS., Jantsch, A. (eds) Fog Computing in the Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-319-57639-8_4
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