Curating E-Mails; A life-cycle approach to the management and preservation of e-mail messages

E-mail forms the backbone of communications in many modern institutions and organisations and is a valuable type of organisational, cultural, and historical record. Successful management and preservation of valuable e-mail messages and collections is therefore vital if organisational accountability is to be achieved and historical or cultural memory retained for the future. This requires attention by all stakeholders across the entire life-cycle of the e-mail records. This instalment of the Digital Curation Manual reports on the several issues involved in managing and curating e-mail messages for both current and future use. Although there is no 'one-size-fits-all' solution, this instalment outlines a generic framework for e-mail curation and preservation, provides a summary of current approaches, and addresses the technical, organisational and cultural challenges to successful e-mail management and longer-term curation.

Enabling Multi-level Trust in Privacy Preserving Data Mining

Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied \emph{perturbation-based PPDM} approach introduces random perturbation to individual values to preserve privacy before data is published. Previous solutions of this approach are limited in their tacit assumption of single-level trust on data miners. In this work, we relax this assumption and expand the scope of perturbation-based PPDM to Multi-Level Trust (MLT-PPDM). In our setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine these diverse copies to jointly infer additional information about the original data that the data owner does not intend to release. Preventing such \emph{diversity attacks} is the key challenge of providing MLT-PPDM services. We address this challenge by properly correlating perturbation across copies at different trust levels. We prove that our solution is robust against diversity attacks with respect to our privacy goal. That is, for data miners who have access to an arbitrary collection of the perturbed copies, our solution prevent them from jointly reconstructing the original data more accurately than the best effort using any individual copy in the collection. Our solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on-demand. This feature offers data owners maximum flexibility.Comment: 20 pages, 5 figures. Accepted for publication in IEEE Transactions on Knowledge and Data Engineerin

プライバシー オ ホゴ スル カウント エンザン ノ タチ ゾクセイ ブンルイ エノ テキヨウ ニ ツイテ

プライバシーを保護しながらデータを効果的に処理することは重要な課題である。本稿では、プライバシー保護のために摂動されたテーブルから、目的属性が3値以上の決定木を構築するために必要なカウント演算結果を再構築する手法を提案する。目的属性が3値以上の場合、従来手法では目的属性の各値の演算結果をそれぞれ独立に再構築しなければならない。そこで、本稿では従来手法を拡張し、目的属性の各値の演算結果を一括して再構築する手法を提案する。It is important to process data effectively while preserving privacy. In this paper, we propose a reconstruction technique of count aggregate queries, which are necessary for building a decision tree, from a perturbed table in cases where a target attribute is more than binary. In the conventional technique, we must reconstruct the results of target values from those of each value calculated independently when a decision tree has a non-binary target attribute. In this paper, we borrow and extend the conventional technique to reconstruct the results of target values at once