| 1. |
- Hallgren, Per, 1988, et al.
(author)
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PrivatePool: Privacy-Preserving Ridesharing
- 2017
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In: Proceedings - IEEE Computer Security Foundations Symposium. - 1940-1434. - 9781538632161 ; , s. 276-291
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Conference paper (peer-reviewed)abstract
- Location-based services have seen tremendous developments over the recent years. These services have revolutionized transportation business, as witnessed by the success of Uber, Lyft, BlaBlaCar, and the like. Yet from the privacy point of view, the state of the art leaves much to be desired. The location of the user is typically shared with the service, opening up for privacy abuse, as in some recently publicized cases. This paper proposes PrivatePool, a model for privacy-preserving ridesharing. We develop secure multi-party computation techniques for endpoint and trajectory matching that allow dispensing with trust to third parties. At the same time, the users learn of a ride segment they can share and nothing else about other users’ location. We establish formal privacy guarantees and investigate how different riding patterns affect the privacy, utility, and performance tradeoffs between approaches based on the proximity of endpoints vs. proximity of trajectories.
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| 2. |
- Lucani, Daniel E., et al.
(author)
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Secure generalized deduplication via multi-key revealing encryption
- 2020
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In: Security and Cryptography for Networks - 12th International Conference, SCN 2020, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030579890 ; 12238 LNCS, s. 298-318
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Conference paper (peer-reviewed)abstract
- Cloud Storage Providers (CSPs) offer solutions to relieve users from locally storing vast amounts of data, including personal and sensitive ones. While users may desire to retain some privacy on the data they outsource, CSPs are interested in reducing the total storage space by employing compression techniques such as deduplication. We propose a new cryptographic primitive that simultaneously realizes both requirements: Multi-Key Revealing Encryption (MKRE). The goal of MKRE is to disclose the result of a pre-defined function over multiple ciphertexts, even if the ciphertexts were generated using different keys, while revealing nothing else about the data. We present a formal model and a security definition for MKRE and provide a construction of MKRE for generalized deduplication that only uses symmetric key primitives in a black-box way. Our construction allows (a) cloud providers to reduce the storage space by using generalized deduplication to compress encrypted data across users, and (b) each user to maintain a certain privacy level for the outsourced information. Our scheme can be proven secure in the random oracle model (and we argue that this is a necessary evil). We develop a proof-of-concept implementation of our solution. For a test data set, our MKRE construction achieves secure generalized deduplication with a compression ratio of 87% for 1 KB file chunks and 82.2% for 8 KB chunks. Finally, our experiments show that, compared to generalized deduplication setup with un-encrypted files, adding privacy via MKRE introduces a compression overhead of less than $$3\%$$ and reduces the storage throughput by at most $$6.9\%$$.
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