
Distributed systems provide users with powerful capabilities to store and process their data in third-party machines. However, the privacy of the outsourced data is not guaranteed. One solution for protecting the user data against privacy attacks is to encrypt the sensitive data before sending to the nodes of the distributed system. Then, the main problem is to evaluate user queries over the encrypted data.In this paper, we propose a complete solution for processing top-k queries over encrypted databases stored across the nodes of a distributed system. The problem of distributed top-k query processing has been well addressed over plaintext (non encrypted) data. However, the proposed approaches cannot be used in the case of encrypted data.
Top-k Query, Distributed System, Privacy, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Sensitive Data
Top-k Query, Distributed System, Privacy, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Sensitive Data
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 3 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
