By Chris Clifton, Balamurugan Anandan (auth.), Aditya Bagchi, Indrakshi Ray (eds.)
This booklet constitutes the refereed lawsuits of the ninth foreign convention on info structures safeguard, ICISS 2013, held in Kolkata, India, in December 2013. The 20 revised complete papers and six brief papers awarded including three invited papers have been conscientiously reviewed and chosen from eighty two submissions. The papers handle theoretical and useful difficulties in info and structures defense and similar areas.
Read Online or Download Information Systems Security: 9th International Conference, ICISS 2013, Kolkata, India, December 16-20, 2013. Proceedings PDF
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Extra info for Information Systems Security: 9th International Conference, ICISS 2013, Kolkata, India, December 16-20, 2013. Proceedings
Client Decryption. Next to functionality as in homomorphic encryption and security as in searchable encryption also eﬃciency can be a reason to add an onion. A bottleneck during encrypted query processing is decrypting the result on the client. It can therefore be more eﬃcient to use a speciﬁc onion with eﬃcient decryption. We use AES in CBC mode as in randomized encryption, but directly on the plaintext. When specifying the conﬁguration of this onion, care must be taken not to use the plaintext for database operations unless intended.
Or alternatively, it may attempt to minimize the risk while ensuring certain performance guarantees. We will illustrate such tradeoffs for a static workload consisting of Hive queries based on our previous work in . As another scenario, consider a system that empowers users with control over data they may store in existing (autonomous) cloud-based services such as Box, Google Drive, Google Calendar, etc. There are many ways to realize such a system – for instance, in our implementation, which we refer to as CloudProtect , the system is implemented as an intermediary privacy middleware that sits between clients and service providers that intercepts the clients’ http requests, transforms requests to suitably encrypt/decrypt the data based on the user’s confidentiality policies before forwarding the request to the service provider.
In a MR setting, sensitive data leakage can occur in multiple ways. It could occur either while storing data in the DFS or during MR processing. For instance, when an input block that contains sensitive data is assigned to a map task running on the public cloud, those sensitive data items will be directly exposed to the public side. Alternatively, if a reducer whose input partition contains sensitive information is assigned to public nodes then that sensitive information would be leaked. Mechanisms to prevent disclosure by using techniques that operate on encrypted data while interesting are not generally applicable.