only the famous ‘right to be left alone’ or keeping one’s personal matters and relationships secret, but also the ability to share information selectivity but not publicly. • Confidentially • Confidentiality is preserving authorized restrictions on information access and disclosure, including means for protecting personal privacy and proprietary information.
benefit society. At the same time, its availability creates significant potential for mistaken, misguided or malevolent uses of personal information. • The conundrum for the law is to provide space for big data to fulfill its potential for social benefit, while protecting citizens adequately from related individual and social harms. Current privacy law evolved to address different concerns and must be adapted to confront big data’s challenges.”
(Personal Identifiable Information) • Any Information About an individual maintained by an agency, including (1) any information that can be used to distinguish or trace an individual’s identity, such as name, social security number, data and place of birth, mother’s maiden name, or biometric records; and (2) any other information that is linked or linkable to an individual, such as medical, educational, financial, and employment information. • 日本の場合 • 保険番号, パスポート番号, 名前, 住所, マイナンバー(ここ数年)
されていない危険性 • “Similarly, overreliance on, say, Twitter Data, in targeting resources after harricanes can lead to misallocation of resources towards young, Internet-savvy people with cell phones and away from elderly or impoverished neighbourhoods” https://azanaerunawano5to4.hatenablog.com/ entry/2015/09/03/101948
and Methods that ensure the confidentiality of micro and aggregated that are to be published. It is methodology used to design statistical outputs in a way that someone with access to that output cannot relate a known individual (or other responding unit) to an element in the output.
Big Data are often structured in such a way that essentially everyone in the file is unique, either because so many variables exist or because they are so frequent or geographically detailed, that they make it easy to reidentify individual pattarns.” • “There are no data stewards controlling access to individual data. Data are often so interconnected (think social media network data) that one person’s action can disclose information about another person without that person even knowing that their data are being accessed.”
instance, an attacker (or commercial actor) might instead infer a rule that relates a string of more easily observable or accessible indicators to a specific medical condition, rendering large populations vulnerable to such inferences even in the absence of PII. Ironically, this is often the very thing about big data that generate the most excitement: the capability to detect subtle correlations and draw actionable inferences. But it is this same feature that renders the traditional protections afforded by anonymity (again, more accurately, pseudosymmetry) much less effective.”
namelessness, and not even in the extension of the previous value of namelessness to all uniquely identifying information, but instead to something we called “reachability, ” the possibility of knocking on your door, hauling you out of bed, calling your phone number, threatening you with sanction, holding you accountable – with or without access to identifying information.
no longer in statistical agencies, with well-defined rules of conduct, but in businesses or administrative agencies. In addition, since digital data can be alive forever, ownership could be claimed by yet-to-be-born relatives whose personal privacy could be threatened by release of information about blood relations.” • “Traditional regulatory tools for managing privacy, notice, and consent have failed to provide a viable market mechanism allowing a form of self-regulation governing industry data collection”
account the varying levels of inherent risk to individuals across different data sets • (2) traditional definitions of PII need to be rethought • (3) regulation has a role in creating and policing walls between data sets • (4) those analyzing big data must be reminded, with a frequency in proportion to the sensitivity of the data, that they are dealing with people • (5) the ethics of big data research must be an open topic for continual reassessment.
• 2019年4月の終わりの日経新聞や, 経産省, 内閣官房の資料で 「引用」 • 一橋大学イノベーション研究センターの Working Paper として 昨年発表 • Yamaguchi, Nitta, Hara, and Shimizu (2018) Staying Young at Heart or Wisdom of Age: Longitudinal. Analysis of Age and Performance in US and Japanese Firms., IIR Working Paper,
R&D rigidity. • The technological distance construct, proposed by Jaffe (1986), was originally intended as a measure of the degree of similarity between technological investment portfolios (which Jaffe called ‘technological positions’) of two different firms. • Thus, we estimated R&D rigidity by calculating the similarity between a firm’s current and previous technological investment portfolio. The more similar a firm’s current and previous portfolios, the more rigid its R&D resource allocation. Technological distance was calculated as follows.
• = 1 , 2 , ⋯ , • F it is 1 ☓ j vector, NP it denotes the number of patents obtained by firm i in year t and NP ijt is the number of patents obtained by firm i in field j in year t • Technological distance (P it ) between firm i’s technological position in year t (F it ) and year t-1 (F it-1 ) is obtained from • = Τ ∙ −1 ′ ∙ ′ −1 ∙ −1 ′ 1/2 • Technological distance assumes a value between 0 and 1, and it is unity if the two vectors are identical, which implies that the firm did not changes its investment profile at all between t-1 and t, and zero if the two vectors are orthogonal, which implies that the firm changed its technological position completely.
yasushihara
botexpress
tbpgr
leveragestech
hashport
kubell_storage
fkino
alpinc
nextbeat
enechain
ldf_tech
recruiting
tokyo_metropolitan_gov_digital_hr
michaelherold
keithpitt
danielanewman
rmw
quramy
jakevdp
mongodb
sugarenia
samlambert
colly
addyosmani
morganepeng
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