Outsourced similarity search on
- 1. Outsourced Similarity Search on Metric Data Assets Abstract— This paper considers a cloud computing setting in which similarity querying of metric data is outsourced to a service provider. The data is to be revealed only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example. Outsourcing offers the data owner scalability and a low-initial investment. The need for privacy may be due to the data being sensitive (e.g., in medicine), valuable (e.g., in astronomy), or otherwise confidential. Given this setting, the paper presents techniques that transform the data prior to supplying it to the service provider for similarity queries on the transformed data. Our techniques provide interesting trade-offs between query cost and accuracy. They are then further extended to offer an intuitive privacy guarantee. Empirical studies with real data demonstrate that the techniques are capable of offering privacy while enabling efficient and accurate processing of similarity queries. Reasons for the proposal : ADVANCES in digital measurement and engineering technologies enable the capture of massive amounts of data in fields such as astronomy, medicine, and seismology. The effort of data collection and processing, as well as its potential utility for research or business, create value for the data owner. He wishes to store them and allow access by himself, colleagues, and other (trusted) scientists or customers. This can be supported by outsourced servers that offer low storage costs
- 2. for large databases. For instance, outsourcing based on cloud computing is becoming increasingly attractive, as it promises pay-as-you-go, low storage costs as well as easy data access. However, care needs to be taken to safeguard data that are valuable or sensitive against unauthorized access. Existing system & demerits: In the literature, a number of concepts for securing databases have been studied. Private information retrieval techniques hide the user’s query, e.g., the data item searched for, but not the data being queried. To outsource valuable data to an insecure server, such techniques are clearly not appropriate. Digital watermarking establishes the data owner’s identity on the data. Additional information stored in the data helps prove ownership, but it cannot prevent an attacker from illegally copying the data set. Anonymization techniques secure data by releasing only a generalized version. Aggregate statistical analysis is still possible on the generalized data, but the result of a specific query is not guaranteed to be accurate. Traditional encryption methods are capable of protecting the confidentiality of the data. However, this also prevents users from querying the data on the untrusted server. Obviously, transferring all the encrypted data to the query user for searching takes outsourcing ad absurdum. Moreover, when services are made available to users on a pay-asyou- go basis, the service providers are not interested in such a brute force data transfer. Proposed system : The goal of this research is to develop a transformation method tðÞ for converting an original object p in a metric space into another metric space object. First, the
- 3. data owner specifies a key value CK in order to define the instance of P to be used. In a preprocessing phase, the data owner computes p0 for each object p and uploads it to the server (i.e., service provider). At query time, the query user specifies his query object q and then submits the transformed query object q0 to the server for similarity search. The transformation method must satisfy these requirements: . Even in the worst case that the attacker knows the inverse of P, he can only estimate the original object p from the transformed object P with bounded precision. . It enables high-query accuracy. . It enables efficient query processing in terms of communication cost. . It supports insertion and deletion of objects. Our contributions are as follows: We present three transformation techniques that satisfy the above requirements. They represent various trade-offs among data privacy and query cost and accuracy. . In our first solution, we propose an encrypted index-based technique with perfect privacy, but multiple communication rounds. This technique flexibly reduces round trip latency at the expense of data transfer. . For our second solution, our private anchor-based indexing guarantees the correct answer within only 2 rounds of communication. Retrieval is accelerated by bounding the range of potential nearest neighbors (NN) in the first phase. . Our third solution limits communication to a single round, and also returns a constant-sized candidate set by computing a close approximation of the query result.