649,632 research outputs found
An Effective Private Data storage and Retrieval System using Secret sharing scheme based on Secure Multi-party Computation
Privacy of the outsourced data is one of the major challenge.Insecurity of
the network environment and untrustworthiness of the service providers are
obstacles of making the database as a service.Collection and storage of
personally identifiable information is a major privacy concern.On-line public
databases and resources pose a significant risk to user privacy, since a
malicious database owner may monitor user queries and infer useful information
about the customer.The challenge in data privacy is to share data with
third-party and at the same time securing the valuable information from
unauthorized access and use by third party.A Private Information Retrieval(PIR)
scheme allows a user to query database while hiding the identity of the data
retrieved.The naive solution for confidentiality is to encrypt data before
outsourcing.Query execution,key management and statistical inference are major
challenges in this case.The proposed system suggests a mechanism for secure
storage and retrieval of private data using the secret sharing technique.The
idea is to develop a mechanism to store private information with a highly
available storage provider which could be accessed from anywhere using queries
while hiding the actual data values from the storage provider.The private
information retrieval system is implemented using Secure Multi-party
Computation(SMC) technique which is based on secret sharing. Multi-party
Computation enable parties to compute some joint function over their private
inputs.The query results are obtained by performing a secure computation on the
shares owned by the different servers.Comment: Data Science & Engineering (ICDSE), 2014 International Conference,
CUSA
Asymmetry Helps: Improved Private Information Retrieval Protocols for Distributed Storage
We consider private information retrieval (PIR) for distributed storage
systems (DSSs) with noncolluding nodes where data is stored using a non maximum
distance separable (MDS) linear code. It was recently shown that if data is
stored using a particular class of non-MDS linear codes, the MDS-PIR capacity,
i.e., the maximum possible PIR rate for MDS-coded DSSs, can be achieved. For
this class of codes, we prove that the PIR capacity is indeed equal to the
MDS-PIR capacity, giving the first family of non-MDS codes for which the PIR
capacity is known. For other codes, we provide asymmetric PIR protocols that
achieve a strictly larger PIR rate compared to existing symmetric PIR
protocols.Comment: To be presented at 2018 IEEE Information Theory Workshop (ITW'18).
See arXiv:1808.09018 for its extended versio
An Empirical Study on Android for Saving Non-shared Data on Public Storage
With millions of apps that can be downloaded from official or third-party
market, Android has become one of the most popular mobile platforms today.
These apps help people in all kinds of ways and thus have access to lots of
user's data that in general fall into three categories: sensitive data, data to
be shared with other apps, and non-sensitive data not to be shared with others.
For the first and second type of data, Android has provided very good storage
models: an app's private sensitive data are saved to its private folder that
can only be access by the app itself, and the data to be shared are saved to
public storage (either the external SD card or the emulated SD card area on
internal FLASH memory). But for the last type, i.e., an app's non-sensitive and
non-shared data, there is a big problem in Android's current storage model
which essentially encourages an app to save its non-sensitive data to shared
public storage that can be accessed by other apps. At first glance, it seems no
problem to do so, as those data are non-sensitive after all, but it implicitly
assumes that app developers could correctly identify all sensitive data and
prevent all possible information leakage from private-but-non-sensitive data.
In this paper, we will demonstrate that this is an invalid assumption with a
thorough survey on information leaks of those apps that had followed Android's
recommended storage model for non-sensitive data. Our studies showed that
highly sensitive information from billions of users can be easily hacked by
exploiting the mentioned problematic storage model. Although our empirical
studies are based on a limited set of apps, the identified problems are never
isolated or accidental bugs of those apps being investigated. On the contrary,
the problem is rooted from the vulnerable storage model recommended by Android.
To mitigate the threat, we also propose a defense framework
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